A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities
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paper_title: A survey of mobile cloud computing: architecture, applications, and approaches

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Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. MCC integrates the cloud computing into the mobile environment and overcomes obstacles related to the performance (e.g., battery life, storage, and bandwidth), environment (e.g., heterogeneity, scalability, and availability), and security (e.g., reliability and privacy) discussed in mobile computing. This paper gives a survey of MCC, which helps general readers have an overview of the MCC including the definition, architecture, and applications. The issues, existing solutions, and approaches are presented. In addition, the future research directions of MCC are discussed. Copyright © 2011 John Wiley & Sons, Ltd.
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paper_title: Opportunities in mobile crowd sensing

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Mobile crowd sensing is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous largescale applications. Human involvement is one of the most important features, and human mobility offers unprecedented opportunities for both sensing coverage and data transmission. In this article, we investigate the opportunistic characteristics of human mobility from the perspectives of both sensing and transmission, and discuss how to exploit these opportunities to collect data efficiently and effectively. We also outline various open issues brought by human involvement in this emerging research area.
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paper_title: How to intelligently make sense of real data of smart cities

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This paper analyzes the benefits of big data for smart cities and the potential of the knowledge discovery from sensed data, which enables real-time systems monitoring, management, optimization and anticipation. In this work we present some examples of applications of big data analysis in two scenarios of smart cities. One of them is focused on the SmartSantander testbed, which offers fixed sensors that record temperature, traffic, and noise. The second example describes the services provided in the SmartCampus of the University of Murcia (UMU), as part of the SMARTIE EU project. The results obtained after applying the most appropriate big data techniques in both scenarios show how it is possible to provide efficiently services like the management of the transport congestion and the energy consumption and comfort of buildings in the context of Smart Cities.
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paper_title: MPCS: Mobile-phone based patient compliance system for chronic illness care

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More than 100 million Americans are currently living with at least one chronic health condition and expenditures on chronic diseases account for more than 75 percent of the $2.3 trillion cost of our healthcare system. To improve chronic illness care, patients must be empowered and engaged in health self-management. However, only half of all patients with chronic illness comply with treatment regimen. The self-regulation model, while seemingly valuable, needs practical tools to help patients adopt this self-centered approach for long-term care. In this position paper, we propose Mobile-phone based Patient Compliance System (MPCS) that can reduce the time-consuming and error-prone processes of existing self-regulation practice to facilitate self-reporting, non-compliance detection, and compliance reminders. The novelty of this work is to apply social-behavior theories to engineer the MPCS to positively influence patients' compliance behaviors, including mobile-delivered contextual reminders based on association theory; mobile-triggered questionnaires based on self-perception theory; and mobile-enabled social interactions based on social-construction theory. We discuss the architecture and the research challenges to realize the proposed MPCS.
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paper_title: Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype

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Imagers are an increasingly significant source of sensory observations about human activity and the urban environment. ImageScape is a software tool for processing, clustering, and browsing large sets of images. Implemented as a set of web services with an Adobe Flash-based user interface, it supports clustering by both image features and context tags, as well as re-tagging of images in the user interface. Though expected to be useful in many applications, ImageScape was designed as an analysis component of DietSense, a software system under development at UCLA to support (1) the use of mobile devices for automatic multimedia documentation of dietary choices with just-in-time annotation, (2) efficient post facto review of captured media by participants and researchers, and (3) easy authoring and dissemination of the automatic data collection protocols. A pilot study, in which participants ran software that enabled their phones to autonomously capture images of their plates during mealtime, was conducted using an early prototype of the DietSense system, and the resulting image set used in the creation of ImageScape. ImageScape will support two kinds of users within the DietSense application: The participants in dietary studies will have the ability to easily audit their images, while the recipients of the images, health care professionals managing studies and performing analysis, will be able to rapidly browse and annotate large sets of images.
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paper_title: Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN

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Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper.
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paper_title: A first look at traffic on smartphones

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Using data from 43 users across two platforms, we present a detailed look at smartphone traffic. We find that browsing contributes over half of the traffic, while each of email, media, and maps contribute roughly 10%. We also find that the overhead of lower layer protocols is high because of small transfer sizes. For half of the transfers that use transport-level security, header bytes correspond to 40% of the total. We show that while packet loss is the main factor that limits the throughput of smartphone traffic, larger send buffers at Internet servers can improve the throughput of a quarter of the transfers. Finally, by studying the interaction between smartphone traffic and the radio power management policy, we find that the power consumption of the radio can be reduced by 35% with minimal impact on the performance of packet exchanges.
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paper_title: ParkSense: a smartphone based sensing system for on-street parking

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Studies of automotive traffic have shown that on average 30% of traffic in congested urban areas is due to cruising drivers looking for parking. While we have witnessed a push towards sensing technologies to monitor real-time parking availability, instrumenting on-street parking throughout a city is a considerable investment. In this paper, we present ParkSense, a smartphone based sensing system that detects if a driver has vacated a parking spot. ParkSense leverages the ubiquitous Wi-Fi beacons in urban areas for sensing unparking events. It utilizes a robust Wi-Fi signature matching approach to detect driver's return to the parked vehicle. Moreover, it uses a novel approach based on the rate of change of Wi-Fi beacons to sense if the user has started driving. We show that the rate of change of the observed beacons is highly correlated with actual user speed and is a good indicator of whether a user is in a vehicle. Through empirical evaluation, we demonstrate that our approach has a significantly smaller energy footprint than traditional location sensors like GPS and Wi-Fi based positioning while still maintaining sufficient accuracy.
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paper_title: From participatory sensing to Mobile Crowd Sensing

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The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS.
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paper_title: Crowdsending based public transport information service in smart cities

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Thanks to the development of technology and the emergence of intelligent services smart cities promise to their inhabitants enhanced perception of city life. For example, a live timetable service of public transportation can increase the efficiency of travel planning substantially. However, its implementation in a traditional way requires the deployment of some costly sensing and tracking infrastructure. Mobile crowdsensing is an alternative, when the crowd of passengers and their mobile devices are used to gather data for almost free of charge. In this paper, we put the emphasis on the introduction of our crowdsensing based public transport information service, what we have been developing as a prototype smart city application. The front-end interface of this service is called TrafficInfo. It is a simple and easy-to-use Android application which visualizes real-time public transport information of the given city on Google Maps. The lively updates of transport schedule information relies on the automatic stop event detection of public transport vehicles. TrafficInfo is built upon our Extensible Messaging and Presence Protocol (XMPP) based communication framework what we designed to facilitate the development of crowd assisted smart city applications. The paper introduces shortly this framework, than describes TrafficInfo in detail together with the developed stop event detector.
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paper_title: A Survey of Incentive Techniques for Mobile Crowd Sensing

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Crowd sensing (CS) is an approach to collecting many samples of a phenomena of interest by distributing the sampling across a large number of individuals. While any one individual may not provide sufficient samples, aggregating samples across many individuals provides high-quality, high-coverage measurements of the phenomena. Thus, for participatory sensing to be successful, one must motivate a large number of individuals to participate. In this work, we review a variety of incentive mechanisms that motivate people to contribute to a CS effort. We then establish a set of design constraints or minimum requirements that any incentive mechanism for CS must have. These design constrains are then used as metrics to evaluate those approaches and determine their advantages and disadvantages. We also contribute a taxonomy of CS incentive mechanisms and show how current systems fit within this taxonomy. We conclude with the identification of new types of incentive mechanisms that require further investigation.
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paper_title: Participatory Air Pollution Monitoring Using Smartphones

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Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on o-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the eect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.
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paper_title: Mobile Phone Sensing Systems: A Survey

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Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction.
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paper_title: Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm

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With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.
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paper_title: Crowdsensing Framework for Monitoring Bridge Vibrations Using Moving Smartphones

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Cities are encountering extensive deficits in infrastructure service while they are experiencing rapid technological advancements and overhauls in transportation systems. Standard bridge evaluation methods rely on visual inspections, which are infrequent and subjective, ultimately affecting the structural assessments on which maintenance plans are based. The operational behavior of a bridge must be observed more regularly and over an extended period in order to sufficiently track its condition and avoid unexpected rehabilitation. Mobile sensor networks are conducive to monitoring bridges vibrations routinely, with benefits that have been demonstrated in recent structural health monitoring (SHM) research. Though smartphone accelerometers are imperfect sensors, they can contribute valuable information to SHM, especially when aggregated, e.g., via crowdsourcing. In an application on the Harvard Bridge (Boston, MA), it is shown that acceleration data collected using smartphones in moving vehicles contained consistent and significant indicators of the first three modal frequencies of the bridge. In particular, the results became more precise when informatics from several smartphone datasets were combined. This evidence is the first to support the hypothesis that smartphone data, collected within vehicles passing over a bridge, can be used to detect several modal frequencies of the bridge. The result defines an opportunity for local governments to make partnerships that encourage the collection of low-cost bridge vibration data, which can contribute to more effective management and informed decision-making.
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paper_title: HazeWatch: A participatory sensor system for monitoring air pollution in Sydney

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This paper describes our experiences with building and deploying a low-cost participatory system for urban air pollution monitoring in Sydney. Though air pollution imposes significant health costs on the urban community globally, today's published data on pollution concentrations is spatially too sparse, and does not allow for sufficiently accurate estimation of exposures for (potentially mobile) individuals in order to make medical inferences. The HazeWatch project described in this paper uses several low-cost mobile sensor units attached to vehicles to measure air pollution concentrations, and users' mobile phones to tag and upload the data in real time. The greater spatial granularity of data thus collected enables creation of pollution maps of metropolitan Sydney viewable in real-time over the web, as well as personalized apps that show the individual's exposure history and allow for route planning to reduce future exposure. We share the insights we obtained from building and trialling such a system in Sydney, and highlight challenges that can be addressed collaboratively by groups developing similar systems world-wide.
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paper_title: QUOIN: Incentive Mechanisms for Crowd Sensing Networks

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Crowd sensing networks play a critical role in big data generation where a large number of mobile devices collect various kinds of data with large-volume features. Although which information should be collected is essential for the success of crowd-sensing applications, few research efforts have been made so far. On the other hand, an efficient incentive mechanism is required to encourage all crowd-sensing participants, including data collectors, service providers, and service consumers, to join the networks. In this article, we propose a new incentive mechanism called QUOIN, which simultaneously ensures Quality and Usability Of INformation for crowd-sensing application requirements. We apply a Stackelberg game model to the proposed mechanism to guarantee each participant achieves a satisfactory level of profits. Performance of QUOIN is evaluated with a case study, and experimental results demonstrate that it is efficient and effective in collecting valuable information for crowd-sensing applications.
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paper_title: ParkGauge: Gauging the Occupancy of Parking Garages with Crowdsensed Parking Characteristics

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Finding available parking spaces in dense urban areas is a globally recognized issue in urban mobility. Whereas prior studies have focused on outdoor/street parking due to a common belief that parking garages are capable of delivering real-time occupancy information, we specifically target at (indoor) parking garages as this belief is far from true. This problem is very challenging as all the infrastructure supports (e.g., GPS and Wi-Fi) assumed by existing proposals are not available to parking garages, so counting how many vehicles are using a parking garage by crowd sensing can be extremely difficult. To this end, we present Park Gauge, a method to gauge the occupancy of parking garages, along with a reference system prototype for performance evaluation, it infers parking occupancy from crowd sensed parking characteristics instead of counting the parked vehicles. Park Gauge adopts low-power sensors (e.g., accelerometer and barometer) in the driver's smartphone to determine the driving states (e.g., turning and braking). A sequence of such states further allows the inference of driving contexts (e.g., driving, queuing and parked) that in turn yield temporal parking characteristics of a parking garage, including time-to-park and time-in-cruising/queuing. Mining such mobile data opportunistically collected from a crowd of drivers arriving at various garages yields a good measure of their occupancies and hence useful recommendations can be generated (in real-time) to inform drivers coming toward these venues. Through extensive experiments, we demonstrate that our method fully explores these parking characteristics to efficiently infer occupancies of parking garages with high accuracy.
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paper_title: WasteApp: Smarter waste recycling for smart citizens

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Waste recycling and disposal is an increasingly pressing issue for the preservation of the Earth natural environment. A considerable amount of research has been carried on factors influencing household waste recycling behaviors and a plethora of supporting tools have been investigated in the last few years. Despite this, recycling waste is still perceived as a cumbersome task, and people around the world are often struggling to find efficient ways to recycle their waste. We argue that, among many factors, a lack of user centered design in waste recycling support tools can be one of the causes. For such reason, we approach the design of a new waste recycling support application, the “WasteApp”, by merging behavioral studies and common features of existing mobile apps with a co-design methodology. We present the outcomes of several co-design sessions aimed at gathering real user needs for waste recycling, an initial design of the “WasteApp” and we discuss the results of a preliminary small-scale user study on the application usability.
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paper_title: Creek watch: pairing usefulness and usability for successful citizen science

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Citizen science projects can collect a wealth of scientific data, but that data is only helpful if it is actually used. While previous citizen science research has mostly focused on designing effective capture interfaces and incentive mechanisms, in this paper we explore the application of HCI methods to ensure that the data itself is useful. To provide a focus for this exploration we designed and implemented Creek Watch, an iPhone application and website that allow volunteers to report information about waterways in order to aid water management programs. Working with state and local officials and private groups involved in water monitoring, we conducted a series of contextual inquiries to uncover what data they wanted, what data they could immediately use, and how to most effectively deliver that data to them. We iteratively developed the Creek Watch application and website based on our findings and conducted evaluations of it with both contributors and consumers of water data, including scientists at the city water resources department. Our study reveals that the data collected is indeed useful for their existing practices and is already in use in water and trash management programs. Our results suggest the application of HCI methods to design the data for the end users is just as important as their use in designing the user interface.
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paper_title: Internet of Things for Smart Cities

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The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.
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paper_title: Incentive Mechanisms for Mobile Crowd Sensing: Current States and Challenges of Work

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Mobile crowd sensing (MCS) is a new paradigm which leverages the ubiquity of sensor-equipped mobile devices such as smartphones, music players, and in-vehicle sensors at the edge of the Internet, to collect data. The new paradigm will fuel the evolution of the Internet of Things to three changes as follows: First, the terminal devices at the edge of the Internet change from PCs to mobile phones. Second, the interactive mode extends from the virtual space to the real physical world. Thirdly, the forwarding manner of sensing data are undergoing the transition from the priori to the opportunistic. To better meet the demands of MCS applications at a societal scale, incentive mechanisms are indispensable. In this paper, we will first overview three categories of MCS applications, and then propose a new architecture for MCS applications. Based on the architecture, we discuss various research challenges about incentive mechanism designs for MCS applications, followed by potential future work discussions. Finally, we present potential future works.
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paper_title: Task Assignment in Mobile Crowdsensing: Present and Future Directions

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Mobile crowdsensing has wide application perspectives and tremendous advantages over traditional sensor networks due to its low cost, extensive coverage, and high sensing accuracy properties. Task assignment is a crucial issue in mobile crowdsensing systems which is intended to achieve a good tradeoff between task quality and task cost. The design of efficient task assignment mechanisms has attracted a lot of attention and much work has been carried out. In this article, we present a comprehensive survey of state-of-the-art task assignment mechanisms in mobile crowdsensing systems. We will first introduce several fundamental issues in task assignment and classify existing mechanisms based on different design criteria. Then we introduce how each of the existing mechanisms works and discuss their merits and deficiencies. Finally, we discuss challenging issues and point out some future directions in this area.
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paper_title: Incentives for Mobile Crowd Sensing: A Survey

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Recent years have witnessed the fast proliferation of mobile devices (e.g., smartphones and wearable devices) in people's lives. In addition, these devices possess powerful computation and communication capabilities and are equipped with various built-in functional sensors. The large quantity and advanced functionalities of mobile devices have created a new interface between human beings and environments. Many mobile crowd sensing applications have thus been designed which recruit normal users to contribute their resources for sensing tasks. To guarantee good performance of such applications, it's essential to recruit sufficient participants. Thus, how to effectively and efficiently motivate normal users draws growing attention in the research community. This paper surveys diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications. The incentives are divided into three categories: entertainment, service, and money. Entertainment means that sensing tasks are turned into playable games to attract participants. Incentives of service exchanging are inspired by the principle of mutual benefits. Monetary incentives give participants payments for their contributions. We describe literature works of each type comprehensively and summarize them in a compact form. Further challenges and promising future directions concerning incentive mechanism design are also discussed.
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paper_title: Crowdsensing in Smart Cities: Overview, Platforms, and Environment Sensing Issues

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Evidence shows that Smart Cities are starting to materialise in our lives through the gradual introduction of the Internet of Things (IoT) paradigm. In this scope, crowdsensing emerges as a powerful solution to address environmental monitoring, allowing to control air pollution levels in crowded urban areas in a distributed, collaborative, inexpensive and accurate manner. However, even though technology is already available, such environmental sensing devices have not yet reached consumers. In this paper, we present an analysis of candidate technologies for crowdsensing architectures, along with the requirements for empowering users with air monitoring capabilities. Specifically, we start by providing an overview of the most relevant IoT architectures and protocols. Then, we present the general design of an off-the-shelf mobile environmental sensor able to cope with air quality monitoring requirements; we explore different hardware options to develop the desired sensing unit using readily available devices, discussing the main technical issues associated with each option, thereby opening new opportunities in terms of environmental monitoring programs.
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paper_title: A survey on privacy in mobile participatory sensing applications

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Abstract: The presence of multimodal sensors on current mobile phones enables a broad range of novel mobile applications. Environmental and user-centric sensor data of unprecedented quantity and quality can be captured and reported by a possible user base of billions of mobile phone subscribers worldwide. The strong focus on the collection of detailed sensor data may however compromise user privacy in various regards, e.g., by tracking a user's current location. In this survey, we identify the sensing modalities used in current participatory sensing applications, and assess the threats to user privacy when personal information is sensed and disclosed. We outline how privacy aspects are addressed in existing sensing applications, and determine the adequacy of the solutions under real-world conditions. Finally, we present countermeasures from related research fields, and discuss their applicability in participatory sensing scenarios. Based on our findings, we identify open issues and outline possible solutions to guarantee user privacy in participatory sensing.
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paper_title: Nericell: rich monitoring of road and traffic conditions using mobile smartphones

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We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.
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paper_title: Third-Eye: A Mobilephone-Enabled Crowdsensing System for Air Quality Monitoring

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Air pollution has raised people's public health concerns in major cities, especially for Particulate Matter under 2.5μm (PM2.5) due to its significant impact on human respiratory and circulation systems. In this paper, we present the design, implementation, and evaluation of a mobile application, Third-Eye, that can turn mobile phones into high-quality PM2.5 monitors, thereby enabling a crowdsensing way for fine-grained PM2.5 monitoring in the city. We explore two ways, crowdsensing and web crawling, to efficiently build large-scale datasets of the outdoor images taken by mobile phone, weather data, and air-pollution data. Then, we leverage two deep learning models, Convolutional Neural Network (CNN) for images and Long Short Term Memory (LSTM) network for weather and air-pollution data, to build an end-to-end framework for training PM2.5 inference models. Our App has been downloaded more than 2,000 times and runs more than 1 year. The real user data based evaluation shows that Third-Eye achieves 17.38 μg/m3 average error and 81.55% classification accuracy, which outperforms 5 state-of-the-art methods, including three scattered interpolations and two image based estimation methods. The results also demonstrate how Third-Eye offers substantial enhancements over typical portable PM2.5 monitors by simultaneously improving accessibility, portability, and accuracy.
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paper_title: Smart cities and the future internet: towards cooperation frameworks for open innovation

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Cities nowadays face complex challenges to meet objectives regarding socio-economic development and quality of life. The concept of "smart cities" is a response to these challenges. This paper explores "smart cities" as environments of open and user-driven innovation for experimenting and validating Future Internet-enabled services. Based on an analysis of the current landscape of smart city pilot programmes, Future Internet experimentally-driven research and projects in the domain of Living Labs, common resources regarding research and innovation can be identified that can be shared in open innovation environments. Effectively sharing these common resources for the purpose of establishing urban and regional innovation ecosystems requires sustainable partnerships and cooperation strategies among the main stakeholders.
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paper_title: HealthAware: Tackling obesity with health aware smart phone systems

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Obesity prevention requires individuals to have healthy eating and physical activity awareness in their daily lives. New technological products have been explored to help monitoring individual health behaviors. Smart phones are ubiq­uitous hand-held tools with rich embedded sensors and limited computing power. A smart phone is a prospective candidate to integrate physical exercise analysis and food intake monitoring. This paper presents a novel health-aware smart phone system (HealthAware) which utilizes the embedded accelerometer to monitor daily physical activities and the built-in camera to analyze food items. The system presents the user's physical activity counts at real time to remind how much daily activity is needed to keep healthy. The user takes pictures of food items and the system provides health related information regarding to the food item intakes. The system is composed of an on-device database which holds the user specific data and food item information. HealthAware is a real time practical system to prevent obesity by enhancing individual daily healthy behavior awareness.
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paper_title: A Review of Mobile Crowdsourcing Architectures and Challenges: Toward Crowd-Empowered Internet-of-Things

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Crowdsourcing using mobile devices, known as mobile crowdsourcing, is a powerful approach incorporating human wisdom into mobile computations to solve problems while exploiting the advantages of mobility and context-awareness. The problems that can be tackled include the use of geographically distributed tasks, and mobile sensing using the collective wisdom of the crowd. However, the implementation of mobile crowdsourcing applications has been found to be challenging to users due to the nature of dynamic sensing, crowd engagement with data distribution, and a process of data verification. In this paper, we provide an extensive survey of the literature on mobile crowdsourcing research, highlighting the aspects of particular concerns in terms of implementation needs during the development, architectures, and key considerations for their development. We present a taxonomy based on the key issues in mobile crowdsourcing and discuss the different approaches applied to these issues. We also provide a critical analysis of some challenges and suggest directions for future work. In particular, with the future Internet-of-Things in view, we generalize the notion of mobile crowdsourcing to thing crowdsourcing, where crowdsourcing can be issued from smart Internet-connected things that need to harness the human resources to solve problems.
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paper_title: A Survey of Mobile Crowdsensing Techniques: A Critical Component for The Internet of Things

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Mobile crowdsensing serves as a critical building block for emerging Internet of Things (IoT) applications. However, the sensing devices continuously generate a large amount of data, which consumes much resources (e.g., bandwidth, energy, and storage) and may sacrifice the Quality-of-Service (QoS) of applications. Prior work has demonstrated that there is significant redundancy in the content of the sensed data. By judiciously reducing redundant data, data size and load can be significantly reduced, thereby reducing resource cost and facilitating the timely delivery of unique, probably critical information and enhancing QoS. This article presents a survey of existing works on mobile crowdsensing strategies with an emphasis on reducing resource cost and achieving high QoS. We start by introducing the motivation for this survey and present the necessary background of crowdsensing and IoT. We then present various mobile crowdsensing strategies and discuss their strengths and limitations. Finally, we discuss future research directions for mobile crowdsensing for IoT. The survey addresses a broad range of techniques, methods, models, systems, and applications related to mobile crowdsensing and IoT. Our goal is not only to analyze and compare the strategies proposed in prior works, but also to discuss their applicability toward the IoT and provide guidance on future research directions for mobile crowdsensing.
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paper_title: Mobile crowd sensing of human-like intelligence using social sensors: A survey

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Abstract Recently, with the fast proliferation of smart phones, mobile phone has the powerful ability of not only communication but also computation. Human beings are not only data consumers, but data producer with their objective or subjective sensing needs. Mobile crowd sensing is an emerging computing paradigm that tasks everyday mobile devices to form participatory sensor networks. It allows the increasing number of mobile phone users to share local knowledge acquired by their sensor-enhanced devices. Social sensors, social sensor receiver platform, and mobile crowd sensing paradigm compose a process by which physical sensors present in mobile devices such as GPS are used to infer social relationships and human activities. In this survey, we review the mobile crowd sensing applications on social sensors based on social sensor receiver platform (e.g., Weibo and Twitter) from three categories: public security, smart city, and location based services. Most applications adopted in current works fit in one of these categories. Existing works on applications of mobile crowd sensing on social sensors are collected and studied. Some possible future directions of potential new application category are proposed and analyzed.
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paper_title: Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm

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With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.
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paper_title: Wireless sensor network survey

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A wireless sensor network (WSN) has important applications such as remote environmental monitoring and target tracking. This has been enabled by the availability, particularly in recent years, of sensors that are smaller, cheaper, and intelligent. These sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the application's design objectives, cost, hardware, and system constraints. The goal of our survey is to present a comprehensive review of the recent literature since the publication of [I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, A survey on sensor networks, IEEE Communications Magazine, 2002]. Following a top-down approach, we give an overview of several new applications and then review the literature on various aspects of WSNs. We classify the problems into three different categories: (1) internal platform and underlying operating system, (2) communication protocol stack, and (3) network services, provisioning, and deployment. We review the major development in these three categories and outline new challenges.
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paper_title: Wireless sensor networks: a survey on recent developments and potential synergies

paper_content:

Wireless sensor network (WSN) has emerged as one of the most promising technologies for the future. This has been enabled by advances in technology and availability of small, inexpensive, and smart sensors resulting in cost effective and easily deployable WSNs. However, researchers must address a variety of challenges to facilitate the widespread deployment of WSN technology in real-world domains. In this survey, we give an overview of wireless sensor networks and their application domains including the challenges that should be addressed in order to push the technology further. Then we review the recent technologies and testbeds for WSNs. Finally, we identify several open research issues that need to be investigated in future. ::: ::: Our survey is different from existing surveys in that we focus on recent developments in wireless sensor network technologies. We review the leading research projects, standards and technologies, and platforms. Moreover, we highlight a recent phenomenon in WSN research that is to explore synergy between sensor networks and other technologies and explain how this can help sensor networks achieve their full potential. This paper intends to help new researchers entering the domain of WSNs by providing a comprehensive survey on recent developments.
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paper_title: Participatory Air Pollution Monitoring Using Smartphones

paper_content:

Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on o-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the eect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.
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paper_title: Mobile Phone Sensing Systems: A Survey

paper_content:

Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction.
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paper_title: Crowdsourcing urban air temperatures from smartphone battery temperatures

paper_content:

[1] Accurate air temperature observations in urban areas are important for meteorology and energy demand planning. They are indispensable to study the urban heat island effect and the adverse effects of high temperatures on human health. However, the availability of temperature observations in cities is often limited. Here we show that relatively accurate air temperature information for the urban canopy layer can be obtained from an alternative, nowadays omnipresent source: smartphones. In this study, battery temperatures were collected by an Android application for smartphones. A straightforward heat transfer model is employed to estimate daily mean air temperatures from smartphone battery temperatures for eight major cities around the world. The results demonstrate the enormous potential of this crowdsourcing application for real-time temperature monitoring in densely populated areas.
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paper_title: Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

paper_content:

Smartphones have become the most pervasive devices in people’s lives and are clearly transforming the way we live and perceive technology. Today’s smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as an accelerometer, a gyroscope, a microphone, and a camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this article, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing and analyze in depth the current state of the art on the topic. We also outline novel research challenges, along with possible directions of future work.
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paper_title: Remote sensing platforms and sensors: A survey

paper_content:

Abstract The objective of this article is to review the state-of-the-art remote sensing technologies, including platforms and sensors, the topics representing the primary research interest in the ISPRS Technical Commission I activities. Due to ever advancing technologies, the remote sensing field is experiencing unprecedented developments recently, fueled by sensor advancements and continuously increasing information infrastructure. The scope and performance potential of sensors in terms of spatial, spectral and temporal sensing abilities have expanded far beyond the traditional boundaries of remote sensing, resulting in significantly better observation capabilities. First, platform developments are reviewed with the main focus on emerging new remote sensing satellite constellations and UAS (Unmanned Aerial System) platforms. Next, sensor georeferencing and supporting navigation infrastructure, an enabling technology for remote sensing, are discussed. Finally, we group sensors based on their spatial, spectral and temporal characteristics, and classify them by their platform deployment competencies. In addition, we identify current trends, including the convergence between the remote sensing and navigation field, and the emergence of cooperative sensing, and the potential of crowdsensing.
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paper_title: Common Sense: participatory urban sensing using a network of handheld air quality monitors

paper_content:

Poor air quality is a global health issue, causing serious problems like asthma, cancer, and heart disease around the world. Earlier this decade, the World Health Organization estimated that three million people die each year from the effects of air pollution [6]. Unfortunately, while variations in air quality are significant, today's air quality monitors are very sparsely deployed. To address this visibility gap, the Common Sense project is developing participatory sensing systems that allow individuals to measure their personal exposure, groups to aggregate their members' exposure, and activists to mobilize grassroots community action.
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paper_title: Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

paper_content:

Today’s mobile phones are far from the mere communication devices they were 10 years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users’ location, activity, social setting, and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.
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paper_title: Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

paper_content:

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.
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paper_title: Incentives for Mobile Crowd Sensing: A Survey

paper_content:

Recent years have witnessed the fast proliferation of mobile devices (e.g., smartphones and wearable devices) in people's lives. In addition, these devices possess powerful computation and communication capabilities and are equipped with various built-in functional sensors. The large quantity and advanced functionalities of mobile devices have created a new interface between human beings and environments. Many mobile crowd sensing applications have thus been designed which recruit normal users to contribute their resources for sensing tasks. To guarantee good performance of such applications, it's essential to recruit sufficient participants. Thus, how to effectively and efficiently motivate normal users draws growing attention in the research community. This paper surveys diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications. The incentives are divided into three categories: entertainment, service, and money. Entertainment means that sensing tasks are turned into playable games to attract participants. Incentives of service exchanging are inspired by the principle of mutual benefits. Monetary incentives give participants payments for their contributions. We describe literature works of each type comprehensively and summarize them in a compact form. Further challenges and promising future directions concerning incentive mechanism design are also discussed.
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paper_title: A survey on privacy in mobile participatory sensing applications

paper_content:

Abstract: The presence of multimodal sensors on current mobile phones enables a broad range of novel mobile applications. Environmental and user-centric sensor data of unprecedented quantity and quality can be captured and reported by a possible user base of billions of mobile phone subscribers worldwide. The strong focus on the collection of detailed sensor data may however compromise user privacy in various regards, e.g., by tracking a user's current location. In this survey, we identify the sensing modalities used in current participatory sensing applications, and assess the threats to user privacy when personal information is sensed and disclosed. We outline how privacy aspects are addressed in existing sensing applications, and determine the adequacy of the solutions under real-world conditions. Finally, we present countermeasures from related research fields, and discuss their applicability in participatory sensing scenarios. Based on our findings, we identify open issues and outline possible solutions to guarantee user privacy in participatory sensing.
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paper_title: Micro-Blog: sharing and querying content through mobile phones and social participation

paper_content:

Recent years have witnessed the impacts of distributed content sharing (Wikipedia, Blogger), social networks (Facebook, MySpace), sensor networks, and pervasive computing. We believe that significant more impact is latent in the convergence of these ideas on the mobile phone platform. Phones can be envisioned as people-centric sensors capable of aggregating participatory as well as sensory inputs from local surroundings. The inputs can be visualized in different dimensions, such as space and time. When plugged into the Internet, the collaborative inputs from phones may enable a high resolution view of the world. This paper presents the architecture and implementation of one such system, called Micro-Blog. New kinds of application-driven challenges are identified and addressed in the context of this system. Implemented on Nokia N95 mobile phones, Micro-Blog was distributed to volunteers for real life use. Promising feedback suggests that Micro-Blog can be a deployable tool for sharing, browsing, and querying global information.
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paper_title: Opportunities in mobile crowd sensing

paper_content:

Mobile crowd sensing is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous largescale applications. Human involvement is one of the most important features, and human mobility offers unprecedented opportunities for both sensing coverage and data transmission. In this article, we investigate the opportunistic characteristics of human mobility from the perspectives of both sensing and transmission, and discuss how to exploit these opportunities to collect data efficiently and effectively. We also outline various open issues brought by human involvement in this emerging research area.
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paper_title: Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype

paper_content:

Imagers are an increasingly significant source of sensory observations about human activity and the urban environment. ImageScape is a software tool for processing, clustering, and browsing large sets of images. Implemented as a set of web services with an Adobe Flash-based user interface, it supports clustering by both image features and context tags, as well as re-tagging of images in the user interface. Though expected to be useful in many applications, ImageScape was designed as an analysis component of DietSense, a software system under development at UCLA to support (1) the use of mobile devices for automatic multimedia documentation of dietary choices with just-in-time annotation, (2) efficient post facto review of captured media by participants and researchers, and (3) easy authoring and dissemination of the automatic data collection protocols. A pilot study, in which participants ran software that enabled their phones to autonomously capture images of their plates during mealtime, was conducted using an early prototype of the DietSense system, and the resulting image set used in the creation of ImageScape. ImageScape will support two kinds of users within the DietSense application: The participants in dietary studies will have the ability to easily audit their images, while the recipients of the images, health care professionals managing studies and performing analysis, will be able to rapidly browse and annotate large sets of images.
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paper_title: Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN

paper_content:

Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper.
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paper_title: From participatory sensing to Mobile Crowd Sensing

paper_content:

The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS.
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paper_title: Mobile Phone Sensing Systems: A Survey

paper_content:

Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction.
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paper_title: Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm

paper_content:

With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.
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paper_title: CrowdSenSim: a Simulation Platform for Mobile Crowdsensing in Realistic Urban Environments

paper_content:

Smart cities take advantage of recent Information and Communication Technology (ICT) developments to provide added value to existing public services and improve quality of life for the citizens. The Internet of Things (IoT) paradigm makes the Internet more pervasive where objects equipped with computing, storage, and sensing capabilities are interconnected with communication technologies. Because of the widespread diffusion of IoT devices, applying the IoT paradigm to smart cities is an excellent solution to build sustainable ICT platforms. Having citizens involved in the process through mobile crowdsensing (MCS) techniques augments capabilities of these ICT platforms without additional costs. For proper operation, MCS systems require the contribution from a large number of participants. Simulations are therefore a candidate tool to assess the performance of MCS systems. In this paper, we illustrate the design of CrowdSenSim, a simulator for mobile crowdsensing. CrowdSenSim is designed specifically for realistic urban environments and smart cities services. We demonstrate the effectiveness of CrowdSenSim for the most popular MCS sensing paradigms (participatory and opportunistic), and we present its applicability using a smart public street lighting scenario.
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paper_title: The participact mobile crowd sensing living lab: The testbed for smart cities

paper_content:

In recent years, the widespread availability of smartphones provided with sensors has enabled the possibility of harvesting large quantities of data in urban areas exploiting user devices, thus enabling so-called mobile crowd sensing (MCS). While many efforts have been made to improve specific techniques for MCS - spanning from signal processing to the assignment of data collection campaigns to users, and to the entire data processing spectrum - to the best of our knowledge, thus far there have been no active experiments of MCS that involve all these techniques in a large-scale real-world experiment. Based on these considerations, we started the ParticipAct Living Lab testbed, an ongoing experiment at the University of Bologna involving 300 students for one year in crowd sensing campaigns that can passively access smartphone sensors and also require active user collaboration. In this article we describe the guidelines behind the design of ParticipAct, as well as its features and architecture. Moreover, we report some of the seminal results gathered during the first three months of its deployment, including accuracy of the classifier provided by ParticipAct client and user inclination to successfully complete tasks depending on the level of active collaboration required to executing them.
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paper_title: Mobile Social Networks: Architectures, Social Properties, and Key Research Challenges

paper_content:

Mobile social networks (MSNs) are mobile communication systems focusing not only on the behaviour but also on the social needs of the users. In a broader view all mobile systems used by people in their everyday lives can be characterized as MSNs, since all interactions taking place follow social rather than random patterns. Whether the deployed communication system takes into account the social background of the underlying network, depends on its form and capabilities. This article presents a review of the relevant work published in MSNs. Initially the basic architectures and components are summarized. Then the basic social properties of the network, as found in the key literature, are extensively examined. These properties are the main source of inspiration for new MSN protocols and applications, especially for non centralized systems. Finally the key research problems and the open issues in the area are presented, including future applications and privacy concerns.
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paper_title: Empowering mobile crowdsensing through social and ad hoc networking

paper_content:

Mobile crowdsensing (MCS) enables collective data harvesting actions by coordinating citizens willing to contribute data collected via their sensor-rich smartphones that represent sources of valuable sensing information in urban environments nowadays. One of the biggest challenges in a real long-running MCS system lies in the capacity not only to attract new volunteers, but also, and most importantly, to leverage existing social ties between volunteers to keep them involved to build long-lasting MCS communities. In addition, the advent of high-performing devices and ad hoc communication technologies can help to further amplify the effect of sensing actions in proximity of the volunteer devices. This article originally describes how to exploit these socio-technical networking aspects to increase the performance of MCS campaigns in the ParticipAct living laboratory, an ongoing MCS real-world experiment that involved about 170 students of the University of Bologna for more than two years. The article also reports some significant experimental results to quantify the effectiveness of the proposed techniques.
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paper_title: A survey on privacy in mobile participatory sensing applications

paper_content:

Abstract: The presence of multimodal sensors on current mobile phones enables a broad range of novel mobile applications. Environmental and user-centric sensor data of unprecedented quantity and quality can be captured and reported by a possible user base of billions of mobile phone subscribers worldwide. The strong focus on the collection of detailed sensor data may however compromise user privacy in various regards, e.g., by tracking a user's current location. In this survey, we identify the sensing modalities used in current participatory sensing applications, and assess the threats to user privacy when personal information is sensed and disclosed. We outline how privacy aspects are addressed in existing sensing applications, and determine the adequacy of the solutions under real-world conditions. Finally, we present countermeasures from related research fields, and discuss their applicability in participatory sensing scenarios. Based on our findings, we identify open issues and outline possible solutions to guarantee user privacy in participatory sensing.
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paper_title: Crowdsensing Simulation Using ns-3

paper_content:

A crowdsensing network is a sensor network in which sensors are users that sense the environment and send the obtained data using, for instance, their smartphones. The performance of such sensor networks depends heavily on the mobility of the users and their willingness to collaborate. It is hard to obtain a stable set of users to evaluate such kinds of sensor networks and, for that reason, studies of crowdsensing networks are scarce. In this paper, we describe how the ns-3 network simulator can be used to simulate some crowdsensing networks with specific characteristics by using the mobility properties of network nodes together with the wireless interface in ad hoc network mode. We model the identification of network nodes with users of the crowdsensing network and we define how to simulate user sensing capabilities. Finally, we present a simulation example for a specific crowdsensing network where users report incidents in the public rail transport.
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paper_title: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

paper_content:

We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.
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paper_title: Trustworthy Sensing for Public Safety in Cloud-Centric Internet of Things

paper_content:

The Internet of Things (IoT) paradigm stands for virtually interconnected objects that are identifiable and equipped with sensing, computing, and communication capabilities. Implementation of services and applications over the IoT architecture can take benefit of the cloud computing concept. Sensing-as-a-Service (S ::: 2 ::: aaS) is a cloud-inspired service model which enables access to the IoT. In this paper, we present a framework where IoT can enhance public safety by crowd management via sensing services that are provided by smart phones equipped with various types of sensors. In order to ensure trustworthiness in the presented framework, we propose a reputation-based (S ::: 2 ::: aaS) scheme, namely, Trustworthy Sensing for Crowd Management (TSCM) for front-end access to the IoT. TSCM collects sensing data based on a cloud model and an auction procedure which selects mobile devices for particular sensing tasks and determines the payments to the users of the mobile devices that provide data. Performance evaluation of TSCM shows that the impact of malicious users in the crowdsourced data can be degraded by 75% while trustworthiness of a malicious user converges to a value below 40% following few auctions. Moreover, we show that TSCM can enhance the utility of the public safety authority up to 85%.
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paper_title: Fostering participaction in smart cities: a geo-social crowdsensing platform

paper_content:

This article investigates how and to what extent the power of collective although imprecise intelligence can be employed in smart cities. The main visionary goal is to automate the organization of spontaneous and impromptu collaborations of large groups of people participating in collective actions (i.e., participAct), such as in the notable case of urban crowdsensing. In a crowdsensing environment, people or their mobile devices act as both sensors that collect urban data and actuators that take actions in the city, possibly upon request. Managing the crowdsensing process is a challenging task spanning several socio-technical issues: from the characterization of the regions under control to the quantification of the sensing density needed to obtain a certain accuracy; from the evaluation of a good balance between sensing accuracy and resource usage (number of people involved, network bandwidth, battery usage, etc.) to the selection of good incentives for people to participAct (monetary, social, etc.). To tackle these problems, this article proposes a crowdsensing platform with three main original technical aspects: an innovative geo-social model to profile users along different variables, such as time, location, social interaction, service usage, and human activities; a matching algorithm to autonomously choose people to involve in participActions and to quantify the performance of their sensing; and a new Android-based platform to collect sensing data from smart phones, automatically or with user help, and to deliver sensing/actuation tasks to users.
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paper_title: Mobile cloud computing service models: a user-centric approach

paper_content:

Mobile devices are rapidly becoming the major service participants nowadays. However, traditional client-server based mobile service models are not able to meet the increasing demands from mobile users in terms of services diversity, user experience, security and privacy, and so on. Cloud computing enables mobile devices to offload complex operations of mobile applications, which are infeasible on mobile devices alone. In this article, we provide a comprehensive study to lay out existing mobile cloud computing service models and key achievements, and present a new user-centric mobile cloud computing service model to advance existing mobile cloud computing research.
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paper_title: A Survey on Software-Defined Wireless Sensor Networks: Challenges and Design Requirements

paper_content:

Software defined networking (SDN) brings about innovation, simplicity in network management, and configuration in network computing. Traditional networks often lack the flexibility to bring into effect instant changes because of the rigidity of the network and also the over dependence on proprietary services. SDN decouples the control plane from the data plane, thus moving the control logic from the node to a central controller. A wireless sensor network (WSN) is a great platform for low-rate wireless personal area networks with little resources and short communication ranges. However, as the scale of WSN expands, it faces several challenges, such as network management and heterogeneous-node networks. The SDN approach to WSNs seeks to alleviate most of the challenges and ultimately foster efficiency and sustainability in WSNs. The fusion of these two models gives rise to a new paradigm: Software defined wireless sensor networks (SDWSN). The SDWSN model is also envisioned to play a critical role in the looming Internet of Things paradigm. This paper presents a comprehensive review of the SDWSN literature. Moreover, it delves into some of the challenges facing this paradigm, as well as the major SDWSN design requirements that need to be considered to address these challenges.
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paper_title: Gyrophone: recognizing speech from gyroscope signals

paper_content:

We show that the MEMS gyroscopes found on modern smart phones are sufficiently sensitive to measure acoustic signals in the vicinity of the phone. The resulting signals contain only very low-frequency information (<200Hz). Nevertheless we show, using signal processing and machine learning, that this information is sufficient to identify speaker information and even parse speech. Since iOS and Android require no special permissions to access the gyro, our results show that apps and active web content that cannot access the microphone can nevertheless eavesdrop on speech in the vicinity of the phone.
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paper_title: Leveraging Crowdsourcing: Activation-Supporting Components for IT-Based Ideas Competition

paper_content:

Ideas competitions appear to be a promising tool for crowdsourcing and open innovation processes, especially for business-to-business software companies. Active participation of potential lead users is the key to success. Yet a look at existing ideas competitions in the software field leads to the conclusion that many information technology (IT)-based ideas competitions fail to meet requirements upon which active participation is established. The paper describes how activation-enabling functionalities can be systematically designed and implemented in an IT-based ideas competition for enterprise resource planning software. We proceeded to evaluate the outcomes of these design measures and found that participation can be supported using a two-step model. The components of the model support incentives and motives of users. Incentives and motives of the users then support the process of activation and consequently participation throughout the ideas competition. This contributes to the successful implementation and maintenance of the ideas competition, thereby providing support for the development of promising innovative ideas. The paper concludes with a discussion of further activation-supporting components yet to be implemented and points to rich possibilities for future research in these areas.
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paper_title: B-Loc: Scalable Floor Localization Using Barometer on Smartphone

paper_content:

Traditional fingerprint based localization techniques mainly rely on infrastructure support such as GSM and Wi-Fi. They require war-driving which is both time-consuming and labor-intensive. With recent advances of smartphone sensors, sensor-assisted localization techniques are emerging. However, they often need user-specific training and more power intensive sensing, resulting in infeasible solutions for real deployment. In this paper, we present B-Loc, a novel floor localization system to identify the floor level in a multi-floor building on which a mobile user is located. It makes use of the barometer on smartphone only. B-Loc does not rely on any Wi-Fi infrastructure and requires neither war-driving nor prior knowledge of the buildings. Leveraging on crowd sourcing, B-Loc builds the barometer fingerprint map which contains the barometric pressure value for each floor level to locate users' floor levels. We conduct both simulation and field studies to demonstrate the accuracy, scalability, and robustness of B-Loc. Our field study in a 10-floor building shows that B-Loc achieves an accuracy of over 98%.
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paper_title: Adaptive context-agnostic floor transition detection on smart mobile devices

paper_content:

We present a technique for detecting floor changes in an indoor environment and improving pedestrian indoor localization and navigation. Our technique relies on barometric pressure sensors commonly available on smartphones and tablets. We developed an algorithm running on smart mobile devices that can be integrated in any indoor localization system to improve accuracy and support 3D navigation. The main novelty of our technique is that it can work in any type of environment, without any prior knowledge of the building layout, it does not require calibration and it is adaptive. Experimental results show that our method can accurately detect floor changes in any condition without requiring any additional work.
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paper_title: Sensing as a Service: Challenges, Solutions and Future Directions

paper_content:

Sensors on (or attached to) mobile phones can enable attractive sensing applications in different domains, such as environmental monitoring, social networking, healthcare, transportation, etc. We introduce a new concept, sensing as a service (S2aaS), i.e., providing sensing services using mobile phones via a cloud computing system. An S2aaS cloud needs to meet the following requirements: 1) it must be able to support various mobile phone sensing applications on different smartphone platforms; 2) it must be energy-efficient; and 3) it must have effective incentive mechanisms that can be used to attract mobile users to participate in sensing activities. In this vision paper, we identify unique challenges of designing and implementing an S2aaS cloud, review existing systems and methods, present viable solutions, and point out future research directions.
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paper_title: Bringing IoT and Cloud Computing towards Pervasive Healthcare

paper_content:

Pervasive healthcare applications utilizing body sensor networks generate a vast amount of data that need to be managed and stored for processing and future usage. Cloud computing among with the Internet of Things (IoT) concept is a new trend for efficient managing and processing of sensor data online. This paper presents a platform based on Cloud Computing for management of mobile and wearable healthcare sensors, demonstrating this way the IoT paradigm applied on pervasive healthcare.
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paper_title: Crowdsourcing user studies with Mechanical Turk

paper_content:

User studies are important for many aspects of the design process and involve techniques ranging from informal surveys to rigorous laboratory studies. However, the costs involved in engaging users often requires practitioners to trade off between sample size, time requirements, and monetary costs. Micro-task markets, such as Amazon's Mechanical Turk, offer a potential paradigm for engaging a large number of users for low time and monetary costs. Here we investigate the utility of a micro-task market for collecting user measurements, and discuss design considerations for developing remote micro user evaluation tasks. Although micro-task markets have great potential for rapidly collecting user measurements at low costs, we found that special care is needed in formulating tasks in order to harness the capabilities of the approach.
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paper_title: Who are the crowdworkers?: shifting demographics in mechanical turk

paper_content:

Amazon Mechanical Turk (MTurk) is a crowdsourcing system in which tasks are distributed to a population of thousands of anonymous workers for completion. This system is increasingly popular with researchers and developers. Here we extend previous studies of the demographics and usage behaviors of MTurk workers. We describe how the worker population has changed over time, shifting from a primarily moderate-income, U.S.-based workforce towards an increasingly international group with a significant population of young, well-educated Indian workers. This change in population points to how workers may treat Turking as a full-time job, which they rely on to make ends meet.
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paper_title: Combining human and machine intelligence in large-scale crowdsourcing

paper_content:

We show how machine learning and inference can be harnessed to leverage the complementary strengths of humans and computational agents to solve crowdsourcing tasks. We construct a set of Bayesian predictive models from data and describe how the models operate within an overall crowd-sourcing architecture that combines the efforts of people and machine vision on the task of classifying celestial bodies defined within a citizens' science project named Galaxy Zoo. We show how learned probabilistic models can be used to fuse human and machine contributions and to predict the behaviors of workers. We employ multiple inferences in concert to guide decisions on hiring and routing workers to tasks so as to maximize the efficiency of large-scale crowdsourcing processes based on expected utility.
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paper_title: Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype

paper_content:

Imagers are an increasingly significant source of sensory observations about human activity and the urban environment. ImageScape is a software tool for processing, clustering, and browsing large sets of images. Implemented as a set of web services with an Adobe Flash-based user interface, it supports clustering by both image features and context tags, as well as re-tagging of images in the user interface. Though expected to be useful in many applications, ImageScape was designed as an analysis component of DietSense, a software system under development at UCLA to support (1) the use of mobile devices for automatic multimedia documentation of dietary choices with just-in-time annotation, (2) efficient post facto review of captured media by participants and researchers, and (3) easy authoring and dissemination of the automatic data collection protocols. A pilot study, in which participants ran software that enabled their phones to autonomously capture images of their plates during mealtime, was conducted using an early prototype of the DietSense system, and the resulting image set used in the creation of ImageScape. ImageScape will support two kinds of users within the DietSense application: The participants in dietary studies will have the ability to easily audit their images, while the recipients of the images, health care professionals managing studies and performing analysis, will be able to rapidly browse and annotate large sets of images.
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paper_title: Accelerometer Based Transportation Mode Recognition on Mobile Phones

paper_content:

Recognizing the transportation modes of people’s daily living is an important research issue in the pervasive computing. Prior research in this field mainly uses Global Positioning System (GPS), Global System for Mobile Communications (GSM) or their combination with accelerometer to recognize transportation modes, such as walking, driving, etc. In this paper, we will introduce transportation mode recognition on mobile phones only using embedded accelerometer. In order to deal with uncertainty of position and orientation of mobile phone, acceleration synthesization based method and acceleration decomposition based method are introduced. Performance comparison indicates that acceleration synthesization based method outperforms acceleration decomposition based method. We will discuss the factors affect the recognition accuracy of acceleration decomposition based method and present potential improvements.
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paper_title: Accelerometer-based transportation mode detection on smartphones

paper_content:

We present novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection. The main performance improvements are obtained for motorised transportation modalities, which currently represent the main challenge for smartphone-based transportation mode detection.
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paper_title: Activity sensing in the wild: a field trial of ubifit garden

paper_content:

Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people's activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.
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paper_title: PerFallD: A pervasive fall detection system using mobile phones

paper_content:

Falls are a major health risk that diminish the quality of life among elderly people. With the elderly population surging, especially with aging “baby boomers”, fall detection becomes increasingly important. However, existing commercial products and academic solutions struggle to achieve pervasive fall detection. In this paper, we propose utilizing mobile phones as a platform for pervasive fall detection system development. To our knowledge, we are the first to do so. We design a detection algorithm based on mobile phone platforms. We propose PerFallD, a pervasive fall detection system implemented on mobile phones. We implement a prototype system on the Android G1 phone and conduct experiments to evaluate our system. In particular, we compare PerFallD's performance with that of existing work and a commercial product. Experimental results show that PerFallD achieves strong detection performance and power efficiency.
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paper_title: An Internet of Things Approach for Managing Smart Services Provided by Wearable Devices

paper_content:

The Internet of Things (IoT) is growing at a fast pace with new devices getting connected all the time. A new emerging group of these devices is the wearable devices, and the wireless sensor networks are a good way to integrate them in the IoT concept and bring new experiences to the daily life activities. In this paper, we present an everyday life application involving a WSN as the base of a novel context-awareness sports scenario, where physiological parameters are measured and sent to the WSN by wearable devices. Applications with several hardware components introduce the problem of heterogeneity in the network. In order to integrate different hardware platforms and to introduce a service-oriented semantic middleware solution into a single application, we propose the use of an enterprise service bus (ESB) as a bridge for guaranteeing interoperability and integration of the different environments, thus introducing a semantic added value needed in the world of IoT-based systems. This approach places all the data acquired (e.g., via internet data access) at application developers disposal, opening the system to new user applications. The user can then access the data through a wide variety of devices (smartphones, tablets, and computers) and operating systems (Android, iOS, Windows, Linux, etc.).
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paper_title: Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

paper_content:

Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).
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paper_title: Crowdsourcing as a Model for Problem Solving -- An Introduction and Cases

paper_content:

Crowdsourcing is an online, distributed problem-solving and production model that has emerged in recent years. Notable examples of the model include Threadless, iStockphoto, InnoCentive, the Goldcorp Challenge, and user-generated advertising contests. This article provides an introduction to crowdsourcing, both its theoretical grounding and exemplar cases, taking care to distinguish crowdsourcing from open source production. This article also explores the possibilities for the model, its potential to exploit a crowd of innovators, and its potential for use beyond forprofit sectors. Finally, this article proposes an agenda for research into crowdsourcing.
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paper_title: Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm

paper_content:

With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.
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paper_title: Activity recognition using cell phone accelerometers

paper_content:

Mobile devices are becoming increasingly sophisticated and the latest generation of smart cell phones now incorporates many diverse and powerful sensors. These sensors include GPS sensors, vision sensors (i.e., cameras), audio sensors (i.e., microphones), light sensors, temperature sensors, direction sensors (i.e., magnetic compasses), and acceleration sensors (i.e., accelerometers). The availability of these sensors in mass-marketed communication devices creates exciting new opportunities for data mining and data mining applications. In this paper we describe and evaluate a system that uses phone-based accelerometers to perform activity recognition, a task which involves identifying the physical activity a user is performing. To implement our system we collected labeled accelerometer data from twenty-nine users as they performed daily activities such as walking, jogging, climbing stairs, sitting, and standing, and then aggregated this time series data into examples that summarize the user activity over 10- second intervals. We then used the resulting training data to induce a predictive model for activity recognition. This work is significant because the activity recognition model permits us to gain useful knowledge about the habits of millions of users passively---just by having them carry cell phones in their pockets. Our work has a wide range of applications, including automatic customization of the mobile device's behavior based upon a user's activity (e.g., sending calls directly to voicemail if a user is jogging) and generating a daily/weekly activity profile to determine if a user (perhaps an obese child) is performing a healthy amount of exercise.
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paper_title: Determining transportation mode on mobile phones

paper_content:

As mobile phones advance in functionality and capability, they are increasingly being used as instruments for personal monitoring. Applications are being developed that take advantage of the sensing capabilities of mobile phones - many have accelerometers, location capabilities, imagers, and microphones - to infer contextual information. We focus on one type of context, the transportation mode of an individual, with the goal of creating a convenient (no requirement to place sensors externally or have specific position/orientation settings) classification system that uses a mobile phone with a GPS receiver and an accelerometer sensor to determine if an individual is stationary, walking, running, biking, or in motorized transport. The target application for this transportation mode inference involves assessing the hazard exposure and environmental impact of an individual's travel patterns. Our prototype classification system consisting of a decision tree followed by a first-order hidden Markov model achieves the application requirement of having accuracy level greater than 90% when testing with our dataset consisting of twenty hours of data collected across six individuals.
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paper_title: An integrated cloud-based framework for mobile phone sensing

paper_content:

Nowadays mobile phones are not only communication devices, but also a source of rich sensory data that can be collected and exploited by distributed people-centric sensing applications. Among them, environmental monitoring and emergency response systems can particularly benefit from people-based sensing. Due to the limited resources of mobile devices, sensed data are usually offloaded to the cloud. However, state-of-the art solutions lack a unified approach suitable to support diverse applications, while reducing the energy consumption of the mobile device. In this paper, we specifically address mobile devices as rich sources of multi-modal data collected by users. In this context, we propose an integrated framework for storing, processing and delivering sensed data to people-centric applications deployed in the cloud. Our integrated platform is the foundation of a new delivery model, namely, Mobile Application as a Service (MAaaS), which allows the creation of people-centric applications across different domains, including participatory sensing and mobile social networks. We specifically address a case study represented by an emergency response system for fire detection and alerting. Through a prototype testbed implementation, we show that the proposed framework can reduce the energy consumption of mobile devices, while satisfying the application requirements.
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paper_title: Smartphone-Based Solutions for Fall Detection and Prevention: Challenges and Open Issues

paper_content:

This paper presents a state-of-the-art survey of smartphone (SP)-based solutions for fall detection and prevention. Falls are considered as major health hazards for both the elderly and people with neurodegenerative diseases. To mitigate the adverse consequences of falling, a great deal of research has been conducted, mainly focused on two different approaches, namely, fall detection and fall prevention. Required hardware for both fall detection and prevention are also available in SPs. Consequently, researchers' interest in finding SP-based solutions has increased dramatically over recent years. To the best of our knowledge, there has been no published review on SP-based fall detection and prevention. Thus in this paper, we present the taxonomy for SP-based fall detection and prevention solutions and systematic comparisons of existing studies. We have also identified three challenges and three open issues for future research, after reviewing the existing articles. Our time series analysis demonstrates a trend towards the integration of external sensing units with SPs for improvement in usability of the systems.
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paper_title: User activity recognition method based on atmospheric pressure sensing

paper_content:

Several studies have been conducted on context recognition as well as hobby and preference extraction by analyzing the data obtained from the sensors in a smartphone. As a smartphone component, a barometer is expected to be useful for activity recognition because of its low power consumption. In this work, we propose an activity recognition method of classifying a user's state into indoor and outdoor states and using a barometer at each state. In the proposed method, the floor of a building on which a user is located is estimated by determining atmospheric pressure variations sensed in the indoor state, and the user's location is estimated by determining atmospheric pressure variations according to the user movement along a track in the outdoor state. In particular, this paper delineates the method of estimating the current floor on which the user is located. We confirmed that it is possible to closely estimate the current floor of the building in the case of user movement among eighteen floors.
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paper_title: Internet of Things for Smart Cities

paper_content:

The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper, we focus specifically to an urban IoT system that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols, and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof-of-concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.
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paper_title: Data Collection in Smart Communities Using Sensor Cloud: Recent Advances, Taxonomy, and Future Research Directions

paper_content:

The remarkable miniaturization of sensors has led to the production of massive amounts of data in smart communities. These data cannot be efficiently collected and processed in WSNs due to the weak communication capability of these networks. This drawback can be compensated for by amalgamating WSNs and cloud computing to obtain sensor clouds. In this article, we investigate, highlight, and report recent premier advances in sensor clouds with respect to data collection. We categorize and classify the literature by devising a taxonomy based on important parameters, such as objectives, applications, communication technology, collection types, discovery, data types, and classification. Moreover, a few prominent use cases are presented to highlight the role of sensor clouds in providing high computation capabilities. Furthermore, several open research challenges and issues, such as big data issues, deployment issues, data security, data aggregation, dissemination of control message, and on time delivery are discussed. Future research directions are also provided.
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paper_title: Sensing services in cloud-centric Internet of Things: A survey, taxonomy and challenges

paper_content:

Virtually interconnected objects with unique identifiers and computing, communication and sensing functionalities form the Internet of Things (IoT) architecture. In the cloud-centric IoT concept, IoT objects can provide sensing services based on a cloud-inspired business model. Smart mobile devices have a great potential to improve the performance of IoT applications by enabling access to their built-in sensor data through cloud servers based on the pay-as-you-go fashion. Such an architecture requires effective sensing service provider search techniques accompanied with effective recruitment algorithms for sensing service providers. Moreover, reliability and trustworthiness of sensing service provisioning is a crucial challenge. This paper provides a brief survey of the state of the art in sensing services over cloud-centric IoT, and presents recent research that addresses the challenges that are mentioned above. Moreover, the paper aims at defining a taxonomy of the surveyed schemes while reporting open issues, existing challenges and possible research directions.
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paper_title: Driving style recognition using a smartphone as a sensor platform

paper_content:

Driving style can characteristically be divided into two categories: “typical” (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone.
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paper_title: Fall detection by built-in tri-accelerometer of smartphone

paper_content:

In this study, a fall detection system based on the data acquired from a waist-mounted smartphone has been developed in a real-time environment. The built-in tri-accelerometer was utilized to collect the information about body movement. At the same time, the smartphone is able to classify the data for activity recognition. Body motion can be classified into five different patterns, i.e. vertical activity, lying, sitting or static standing, horizontal activity and fall. If a fall is suspected, an automatic Multimedia Messaging Service (MMS) will be sent to pre-selected people, with information including the time, GPS coordinate, and Google map of suspected fall location. The major advantage of the proposed system is the use of smartphone which is readily available to most people.
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paper_title: SIaaS - Sensing Instrument as a Service Using Cloud Computing to Turn Physical Instrument into Ubiquitous Service

paper_content:

Sensing Instrument as a Service (SIaaS) is a new cloud service that allows users to use data acquisition instruments shared through cloud infrastructure. It offers a common interface to managing physical sensing instrument and it permits to take all advantages of using cloud computing technology in storing and processing acquired data. With SIaaS different research groups could share physical instrument, in a controlled way, event if they are geographically distributed and user can access to them using an internet connected device without the need to install any sort of program. With the interaction of different own framework we have created a new cloud service for transforming physical resources in a ubiquitous service.
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paper_title: DemonicSalmon: Monitoring mental health and social interactions of college students using smartphones

paper_content:

Abstract Mental health problems are a leading cause of disease burden and disability worldwide. Social anxiety and depression are highly prevalent among college students. The current methods for detecting symptoms are based on client self-report via questionnaires and interviews in traditional clinical settings, but self-report is subject to recall bias and visiting a clinic requires a high level of motivation. Assessment methods that use both actively and passively collected data hold promise for detecting and monitoring social anxiety and depression symptoms as individuals go about their daily lives. The present study, named DemonicSalmon, investigates how social anxiety and depression symptoms manifest in the daily life of 72 students over a two-week study period. Results show a number of significant correlations between the automatic objective sensor data from smartphones and indicators of mental health. The collected data enhances understanding of how students’ social anxiety, depression and affect levels are associated with their mobility, activity levels, and communication patterns. The DemonicSalmon dataset has been made publicly available on the web to enhance collaborations in this important area of research.
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paper_title: Human-in-the-Loop Mobile Networks: A Survey of Recent Advancements

paper_content:

Recent developments of smart devices and mobile applications have significantly increased the level at which human users interact with mobile systems. As a result, human activities, usage behavior, and perceived experience of users weigh increasingly on the performance of mobile networks, which has created new challenges for system operation in various aspects, such as increasing uncertainty, selfishness in operations, and complicated performance evaluation. On the other hand, the strong engagement of a large population of human users makes it possible to take advantage of the unique features of human behavior and to leverage the computing powers owned by users. Due to these emerging features of mobile networks, their design and evaluation require a hybrid view of human factor and information technology, and a paradigm shift is required for designing a new human-in-the-loop architecture by actively learning, adapting, and steering user behavior, so as to exploit the human factor in future ubiquitous mobile systems, and to greatly enhance system efficiency and provide superior quality-of-experience to users. The goal of this survey is to summarize recent results that focus on understanding and exploiting the human factor in mobile networks. In the tutorial, we summarize and discuss novelties of these formulations, adopted methodologies, and interesting results. We also point out some future research directions.
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paper_title: Using mobile phones to determine transportation modes

paper_content:

As mobile phones advance in functionality and capability, they are being used for more than just communication. Increasingly, these devices are being employed as instruments for introspection into habits and situations of individuals and communities. Many of the applications enabled by this new use of mobile phones rely on contextual information. The focus of this work is on one dimension of context, the transportation mode of an individual when outside. We create a convenient (no specific position and orientation setting) classification system that uses a mobile phone with a built-in GPS receiver and an accelerometer. The transportation modes identified include whether an individual is stationary, walking, running, biking, or in motorized transport. The overall classification system consists of a decision tree followed by a first-order discrete Hidden Markov Model and achieves an accuracy level of 93.6p when tested on a dataset obtained from sixteen individuals.
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paper_title: Sensing as a Service: Challenges, Solutions and Future Directions

paper_content:

Sensors on (or attached to) mobile phones can enable attractive sensing applications in different domains, such as environmental monitoring, social networking, healthcare, transportation, etc. We introduce a new concept, sensing as a service (S2aaS), i.e., providing sensing services using mobile phones via a cloud computing system. An S2aaS cloud needs to meet the following requirements: 1) it must be able to support various mobile phone sensing applications on different smartphone platforms; 2) it must be energy-efficient; and 3) it must have effective incentive mechanisms that can be used to attract mobile users to participate in sensing activities. In this vision paper, we identify unique challenges of designing and implementing an S2aaS cloud, review existing systems and methods, present viable solutions, and point out future research directions.
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paper_title: Sensing and actuation as a service delivery model in Cloud Edge centric Internet of Things

paper_content:

Abstract Internet of Things (IoT) is an advanced innovation of the Internet which allows communications among all living or non-living things. The smart devices have become powerful and intelligent that they can sense, communicate, compute and actuate among themselves to provide a smart environment. So, in short, it could be possibly known as the Internet of Everything (IoE). However, with the current architecture, the contributors are not motivated to share the sensed data or provide their actuators to others as a service. The security and various node managements are some of the main issues to be addressed for motivating the contributors. Therefore, we introduce “Sensing and Actuation as a Service Delivery Model (SAaaSDM)”, which is a cloud edge-centric service delivery model. It authorizes access to the IoT Architecture (IoT-A), where sensed, actuated, and computed data from various existing mobile devices can be used by the end user through SAaaSDM on a pay as you go fashion. Participatory node management, virtual node management, and quality review management are the emerging components of this architecture. Participatory nodes along with device owners claim for various challenges like cost, reliability, trustworthiness, quality, utility etc. Similarly, the expectations of the end users also appear as a big challenge. In this paper, we present the SAaaSDM system model which can deal with open issues and also discuss the future directions for the researchers in this field.
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paper_title: SenaaS: An event-driven sensor virtualization approach for Internet of Things cloud

paper_content:

In this paper, we propose an Internet of Things (IoT) virtualization framework to support connected objects sensor event processing and reasoning by providing a semantic overlay of underlying IoT cloud. The framework uses the sensor-as-aservice notion to expose IoT cloud's connected objects functional aspects in the form of web services. The framework uses an adapter oriented approach to address the issue of connectivity with various types of sensor nodes. We employ semantic enhanced access polices to ensure that only authorized parties can access the IoT framework services, which result in enhancing overall security of the proposed framework. Furthermore, the use of event-driven service oriented architecture (e-SOA) paradigm assists the framework to leverage the monitoring process by dynamically sensing and responding to different connected objects sensor events. We present our design principles, implementations, and demonstrate the development of IoT application with reasoning capability by using a green school motorcycle (GSMC) case study. Our exploration shows that amalgamation of e-SOA, semantic web technologies and virtualization paves the way to address the connectivity, security and monitoring issues of IoT domain.
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paper_title: A novel on-demand vehicular sensing framework for traffic condition monitoring

paper_content:

Abstract With the increased need for mobility and the overcrowding of cities, the area of Intelligent Transportation aims at improving the efficiency, safety, and productivity of transportation systems by relying on communication and sensing technologies. One of the main challenges faced in Intelligent Transportation Systems (ITS) pertains to the real time collection of traffic and road related data, in a cost effective, efficient, and scalable manner. The current approaches still suffer from problems related to the mobile devices energy consumption and overhead in terms of communications and processing. To tackle the aforementioned challenges, we propose in this paper a novel infrastructure-less on-demand vehicular sensing framework that provides accurate road condition monitoring, while reducing the number of participating vehicles, energy consumption, and communication overhead. Our approach is adopting the concept of Mobile Sensing as a Service (MSaaS), in which mobile owners participate in the data collection activities and decide to offer the sensing capabilities of their phones as services to other users. Unlike existing approaches that rely on opportunistic continuous sensing from all available cars, this ability to offer sensory data to consumers on demand can bring significant benefits to ITS and can constitute an efficient and flexible solution to the problem of real-time traffic/road data collection. A combination of prototyping and traffic simulation traces are used to realize the system, and a variety of test cases are used to evaluate its performance. When compared to the traditional continuous sensing, our proposed on-demand sensing approach provides comparable high traffic estimation accuracy while significantly reducing the resource consumption. Based on the obtained results, using the on-demand sensing approach with 30% of cars as participants in the sensing activity, and a six-criteria matching approach yields a reduction of 73.8% in terms of network load and a reduction of 60.3% in terms of response time (when compared to the continuous sensing approach), while achieving a traffic estimation accuracy of 81.71%.
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paper_title: An integrated cloud-based framework for mobile phone sensing

paper_content:

Nowadays mobile phones are not only communication devices, but also a source of rich sensory data that can be collected and exploited by distributed people-centric sensing applications. Among them, environmental monitoring and emergency response systems can particularly benefit from people-based sensing. Due to the limited resources of mobile devices, sensed data are usually offloaded to the cloud. However, state-of-the art solutions lack a unified approach suitable to support diverse applications, while reducing the energy consumption of the mobile device. In this paper, we specifically address mobile devices as rich sources of multi-modal data collected by users. In this context, we propose an integrated framework for storing, processing and delivering sensed data to people-centric applications deployed in the cloud. Our integrated platform is the foundation of a new delivery model, namely, Mobile Application as a Service (MAaaS), which allows the creation of people-centric applications across different domains, including participatory sensing and mobile social networks. We specifically address a case study represented by an emergency response system for fire detection and alerting. Through a prototype testbed implementation, we show that the proposed framework can reduce the energy consumption of mobile devices, while satisfying the application requirements.
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paper_title: Sensing as a Service and Big Data

paper_content:

Internet of Things (IoT) will comprise billions of devices that can sense, communicate, compute and potentially actuate. Data streams coming from these devices will challenge the traditional approaches to data management and contribute to the emerging paradigm of big data. This paper discusses emerging Internet of Things (IoT) architecture, large scale sensor network applications, federating sensor networks, sensor data and related context capturing techniques, challenges in cloud-based management, storing, archiving and processing of sensor data.
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paper_title: Assessing Performance of Internet of Things-Based Mobile Crowdsensing Systems for Sensing as a Service Applications in Smart Cities

paper_content:

The Internet of Things (IoT) paradigm makes the Internet more pervasive. IoT devices are objects equipped with computing, storage and sensing capabilities and they are interconnected with communication technologies. Smart cities exploit the most advanced information technologies to improve public services. For being effective, smart cities require a massive amount of data, typically gathered from sensors. The application of the IoT paradigm to smart cities is an excellent solution to build sustainable Information and Communication Technology (ICT) platforms and to produce a large amount of data following Sensing as a Service (S$^2$aaS) business models. Having citizens involved in the process through mobile crowdsensing (MCS) techniques unleashes potential benefits as MCS augments the capabilities of existing sensing platforms. To this date, it remains an open challenge to quantify the costs the users sustain to contribute data with IoT devices such as the energy from the batteries and the amount of data generated at city-level. In this paper, we analyze existing solutions, we provide guidelines to design a large-scale urban level simulator and we present preliminary results from a prototype.
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paper_title: Taming the uncertainty: budget limited robust crowdsensing through online learning

paper_content:

Mobile crowdsensing has been intensively explored recently due to its flexible and pervasive sensing ability. Although many crowdsensing platforms have been built for various applications, the general issue of how to manage such systems intelligently remains largely open. While recent investigations mostly focus on incentivizing crowdsensing, the robustness of crowdsensing toward uncontrollable sensing quality, another important issue, has been widely neglected. Due to the non-professional personnel and devices, the quality of crowdsensing data cannot be fully guaranteed, hence the revenue gained from mobile crowdsensing is generally uncertain. Moreover, the need for compensating the sensing costs under a limited budget has exacerbated the situation: one does not enjoy an infinite horizon to learn the sensing ability of the crowd and hence to make decisions based on sufficient statistics. In this paper, we present a novel framework, Budget LImited robuSt crowdSensing (BLISS), to handle this problem through an online learning approach. Our approach aims to minimize the difference on average sense (a.k.a. regret) between the achieved total sensing revenue and the (unknown) optimal one, and we show that our BLISS sensing policies achieve logarithmic regret bounds and Hannan-consistency. Finally, we use extensive simulations to demonstrate the effectiveness of BLISS.
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paper_title: Sensing and Actuation as a Service: A New Development for Clouds

paper_content:

Cloud computing is among the hottest trends in ICT, aiming at providing on-demand computing and storage resources with guarantees on the quality of service. A limit of current Cloud implementations is the absence of mechanisms to effectively manage inputs from the physical world. Our idea is to move towards a pervasive Cloud, providing facilities and solutions able to interact with the surrounding environment enabling development of new and value added services. In this vision also mobile devices, such as PDAs, usually equipped with several sensors and actuators, have to be included in the overall picture. Mobile devices and their respective owners can decide whether, how and when to contribute to the Cloud, thus introducing further unknowns. In order to deal with all such issues, in this paper we propose a solution that gives way to the Sensing and Actuation as a Service (SAaaS) paradigm, a step towards the creation of a Cloud of sensors and actuators. This paper mainly focuses on the implementation of the underlying infrastructure at the basis of the SAaaS. An ad-hoc architecture and some preliminary background on this challenging vision are provided and discussed.
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paper_title: SIaaS - Sensing Instrument as a Service Using Cloud Computing to Turn Physical Instrument into Ubiquitous Service

paper_content:

Sensing Instrument as a Service (SIaaS) is a new cloud service that allows users to use data acquisition instruments shared through cloud infrastructure. It offers a common interface to managing physical sensing instrument and it permits to take all advantages of using cloud computing technology in storing and processing acquired data. With SIaaS different research groups could share physical instrument, in a controlled way, event if they are geographically distributed and user can access to them using an internet connected device without the need to install any sort of program. With the interaction of different own framework we have created a new cloud service for transforming physical resources in a ubiquitous service.
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paper_title: A Review of Mobile Crowdsourcing Architectures and Challenges: Toward Crowd-Empowered Internet-of-Things

paper_content:

Crowdsourcing using mobile devices, known as mobile crowdsourcing, is a powerful approach incorporating human wisdom into mobile computations to solve problems while exploiting the advantages of mobility and context-awareness. The problems that can be tackled include the use of geographically distributed tasks, and mobile sensing using the collective wisdom of the crowd. However, the implementation of mobile crowdsourcing applications has been found to be challenging to users due to the nature of dynamic sensing, crowd engagement with data distribution, and a process of data verification. In this paper, we provide an extensive survey of the literature on mobile crowdsourcing research, highlighting the aspects of particular concerns in terms of implementation needs during the development, architectures, and key considerations for their development. We present a taxonomy based on the key issues in mobile crowdsourcing and discuss the different approaches applied to these issues. We also provide a critical analysis of some challenges and suggest directions for future work. In particular, with the future Internet-of-Things in view, we generalize the notion of mobile crowdsourcing to thing crowdsourcing, where crowdsourcing can be issued from smart Internet-connected things that need to harness the human resources to solve problems.
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paper_title: SenSquare: A mobile crowdsensing architecture for smart cities

paper_content:

In the recent years the Smart City paradigm has gained interest worldwide. Services are built on top of data sensed in the city and then analyzed in order to enhance people's quality of life. Nowadays users are also able to participate in such a data gathering, mostly thanks to a reduction in the cost of the sensing devices. Moreover, smartphones encompass many useful sensors and can be leveraged to obtain data by end users on the move, within the scope of mobile crowdsensing. In this paper, we propose SenSquare, a mobile crowdsensing architecture for Smart Cities, built to embrace both data availability and devices heterogeneity. SenSquare also offers the possibility for stakeholders to reward users sharing their data. Finally, we compare our proposal against a non-smart ideal architecture, showing the benefits and the advantages of a smart architecture such as SenSquare.
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paper_title: Energy Saving Techniques in Mobile Crowd Sensing: Current State and Future Opportunities

paper_content:

With the prevalence of sensor-rich smartphones, MCS has become an emerging paradigm to perform urban sensing tasks in recent years. In MCS systems, it is important to minimize the energy consumption on devices of mobile users, as high energy consumption severely reduces their participation willingness. In this article, we provide a comprehensive review of energy saving techniques in MCS and identify future research opportunities. Specifically, we analyze the main causes of energy consumption in MCS and present a general energy saving framework named ESCrowd that we use to describe the different detailed MCS energy saving techniques. We further present how the various energy saving techniques are utilized and adopted within MCS applications and point out their existing limitations, which inform and guide future research directions.
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paper_title: Counter-strike: accurate and robust identification of low-level radiation sources with crowd-sensing networks

paper_content:

The use of crowd-sensing networks is a promising and low-cost way for identifying low-level radiation sources, which is greatly important for the security protection of modern cities. However, it is challenging to identify radiation sources based on inaccurate crowd-sensing measurements with unknown sensor efficiency, due to uncontrollable nature of users. However, existing methods assume the sensor efficiency is available, while their identification accuracy tightly depends on identification threshold. To address these problems, we present Counter-Strike, an accurate and robust identification method. Specifically, we use truthful probability of sources for robust identification. And then, we propose an iterative truthful-source identification algorithm, alternately iterating between sensor efficiency estimation and truthful probability estimation, gradually improving the identification accuracy. The extensive simulations and theoretical analysis show that our method can converge into the maximum likelihood of crowd-sensing measurements, achieving much higher identification accuracy than the existing methods. Further, the identification threshold makes slight influence on the identification accuracy in our method, facilitating its practical use.
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paper_title: NoiseTube: Measuring and mapping noise pollution with mobile phones

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In this paper we present a new approach for the assessment of noise pollution involving the general public. The goal of this project is to turn GPS-equipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment. Thus each user can contribute by sharing their geo-localised measurements and further personal annotation to produce a collective noise map.
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paper_title: A Collaborative Internet of Things Architecture for Smart Cities and Environmental Monitoring

paper_content:

The collaborative Internet of Things (C-IoT) is an emerging paradigm that involves many communities with the idea of cooperating in data gathering and service sharing. Many fields of application, such as smart cities and environmental monitoring, use the concept of crowdsensing in order to produce the amount of data that such Internet of Things (IoT) scenarios need in order to be pervasive. In this paper we introduce an architecture, namely SenSquare, able to handle both the heterogeneous data sources coming from open IoT platform and crowdsensing campaigns, and display a unified access to users. We inspect all the facets of such a complex system, spanning over issues of different nature: we deal with heterogeneous data classification, mobile crowdsensing management for environmental data, information representation, and unification, IoT service composition and deployment. We detail our proposed solution in dealing with such tasks and present possible methods for meeting open challenges. Finally, we demonstrate the capabilities of SenSquare through both a mobile and a desktop client.
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paper_title: Participatory Sensing in Commerce: Using Mobile Camera Phones to Track Market Price Dispersion

paper_content:

In economics, price dispersion refers to the price difference of a homogeneous good across different vendors. According to [1] “The empirical evidence suggests that price dispersion in both online and offline markets is sizeable, pervasive, and persistent.” Not surprisingly, there exist several popular web commerce sites such as Froogle that enable users to track consumer pricing information in online markets. In this paper, we present and explore our vision that participatory sensing can be employed in this new application domain to track price dispersion in homogeneous consumer goods even in offline markets. We discuss two proof-of-concept participatory mobile camera-phone sensing systems that we have built: (1) automating fuel price collection, and (2) semi-automated scanning of receipts.
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paper_title: Urban WiFi characterization via mobile crowdsensing

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We present a mobile crowdsensing approach for urban WiFi characterization that leverages commodity smartphones and the natural mobility of people. Specifically, we report measurement results obtained for Edinburgh, a representative European city, on detecting the presence of deployedWiFi APs via the mobile crowdsensing approach. They show that few channels in 2.4GHz are heavily used; in contrast, there is hardly any activity in the 5GHz band even though relatively it has a greater number of available channels. Spatial analysis of spectrum usage reveals that mutual interference among nearby APs operating in the same channel can be a serious problem with around 10 APs contending with each other in many locations. We find that the characteristics of WiFi deployments at city-scale are similar to that of WiFi deployments in public spaces of different indoor environments. We validate our approach in comparison with wardriving, and also show that our findings generally match with previous studies based on other measurement approaches. As an application of the mobile crowdsensing based urban WiFi monitoring, we outline a cloud based WiFi router configuration service for better interference management with global awareness in urban areas.
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paper_title: Dynamic Trust Relationships Aware Data Privacy Protection in Mobile Crowd-Sensing

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Malicious network nodes often incur problems to network and data privacy by distributing forged public keys. To address this issue, this paper proposes a dynamic trust relationships aware data privacy protection (DTRPP) mechanism for mobile crowd-sensing. In this mechanism, combining key distribution with trust management, the trust value of a public key is evaluated according to both the number of supporter and the trust degree of the public key. The trust value is estimated from the accuracy of the public key provided by the encountering nodes. DTRPP achieves the dynamic management of nodes and estimates the trust degree of the public key. In addition, by classifying traffic data into different types and selecting a proper relay node to forward the data according to data types, it is more effective to use the network resource with the trust degree and centrality of the relays. With extensive evaluations, results show that the proposed mechanism protects the data privacy effectively and has better performance on the average delay, the delivery rate and the loading rate when compared to traditional mechanisms.
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paper_title: Urban Computing: Concepts, Methodologies, and Applications

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Urbanization's rapid progress has modernized many people's lives but also engendered big issues, such as traffic congestion, energy consumption, and pollution. Urban computing aims to tackle these issues by using the data that has been generated in cities (e.g., traffic flow, human mobility, and geographical data). Urban computing connects urban sensing, data management, data analytics, and service providing into a recurrent process for an unobtrusive and continuous improvement of people's lives, city operation systems, and the environment. Urban computing is an interdisciplinary field where computer sciences meet conventional city-related fields, like transportation, civil engineering, environment, economy, ecology, and sociology in the context of urban spaces. This article first introduces the concept of urban computing, discussing its general framework and key challenges from the perspective of computer sciences. Second, we classify the applications of urban computing into seven categories, consisting of urban planning, transportation, the environment, energy, social, economy, and public safety and security, presenting representative scenarios in each category. Third, we summarize the typical technologies that are needed in urban computing into four folds, which are about urban sensing, urban data management, knowledge fusion across heterogeneous data, and urban data visualization. Finally, we give an outlook on the future of urban computing, suggesting a few research topics that are somehow missing in the community.
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paper_title: Improving location reliability in crowd sensed data with minimal efforts

paper_content:

People-centric sensing with smart phones can be used for large scale sensing of the physical world by leveraging the cameras, microphones, GPSs, accelerometers, and other sensors on the phones. Ranging from manual photo tasks to automated sensing tasks for activity monitoring, any task can be crowd sourced to smart phones to sense data from different locations at reduced cost. However, the sensed data submitted by participants is not always reliable as they can submit false data to earn money without executing the actual task. Therefore, it is important to validate the sensed data. Validating the context of every sensed data point of each participant is not a scalable solution. One alternative is to first validate the location associated with the sensed data points in order to achieve a certain degree of reliability about the sensed data. However, location validation without support from the wireless carriers is difficult. To address this problem, we propose ILR, a scheme in which we Improve the Location Reliability of mobile crowd sensed data with minimal human efforts. In this scheme, we bootstrap the trust in the system by first manually or automatically using image processing techniques validating a small number of photos submitted by participants. Based on these validations, the location of these photos is assumed to be trusted. Second, we extend this location trust to co-located sensed data points found in the Bluetooth range of the devices that provided the validated photos. This transitive trust is extended until all the co-located tasks are trusted or no new data points are found. In addition, the scheme also helps to detect false location claims associated with sensed data. We applied ILR on data collected from our McSense prototype deployed on Android phones used by students on our campus and detected a significant percentage of the malicious users. Simulation results demonstrate that ILR works well at various densities and helps detect the false location claims based on a minimal numher of validations.
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paper_title: Micro-Blog: sharing and querying content through mobile phones and social participation

paper_content:

Recent years have witnessed the impacts of distributed content sharing (Wikipedia, Blogger), social networks (Facebook, MySpace), sensor networks, and pervasive computing. We believe that significant more impact is latent in the convergence of these ideas on the mobile phone platform. Phones can be envisioned as people-centric sensors capable of aggregating participatory as well as sensory inputs from local surroundings. The inputs can be visualized in different dimensions, such as space and time. When plugged into the Internet, the collaborative inputs from phones may enable a high resolution view of the world. This paper presents the architecture and implementation of one such system, called Micro-Blog. New kinds of application-driven challenges are identified and addressed in the context of this system. Implemented on Nokia N95 mobile phones, Micro-Blog was distributed to volunteers for real life use. Promising feedback suggests that Micro-Blog can be a deployable tool for sharing, browsing, and querying global information.
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paper_title: LiveCompare: grocery bargain hunting through participatory sensing

paper_content:

Many consumers are misled into paying high prices due to the search costs associated with attaining price information [16]. The popularity of bargain-hunting web sites like Slickdeals.net, which boasts 2.5 million visitors per month, hints that many shoppers are indeed in search of tools to help them save money. We present LiveCompare, a system that leverages the ubiquity of mobile camera phones to allow for grocery bargain hunting through participatory sensing. We utilize two-dimensional barcode decoding to automatically identify grocery products, as well as localization techniques to automatically pinpoint store locations. We show that an incentive scheme is inherently ingrained into our query/response protocol, and we suggest self-regulating mechanisms for preserving data integrity. As a result, we demonstrate that money-saving price comparisons can be conducted among brick and mortar grocery stores without the explicit cooperation of the stores.
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paper_title: Resource efficient mobile communications for crowd-sensing

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Due to continuously growing communication traffic of emerging mobile phone applications, resource-efficient communication is a key to affordable network services with high Quality of Experience. While some communication traffic requires immediate resource allocations (such as voice services), an increasing number of mobile phone applications produce a lot of background communication traffic (e.g. social network apps, crowd sensing services). In this paper we discuss the predictive Channel-Aware Transmission (pCAT) scheme, which leverages the fact, that favorable channel conditions require much less spectrum resource allocation than bad channel conditions. By leveraging the knowledge of user trajectories and recurring spots with favorable channel conditions, the so-called LTE connectivity hot spots, background traffic transmissions can be scheduled by the client according to expected channel quality and application data priority. Thereby, the spectrum consumption of background traffic of applications can be reduced significantly. At the same time, the efficient usage of spectrum resources has also an impact on the battery lifetime of the mobile devices. By introducing the Context-Aware Power Consumption Model (CoPoMo) for LTE communications, we highlight, how decisions about the spectrum resource allocation by the network will impact the battery lifetime. One case study will show, that by simply changing the resource allocation scheme and without the need for spending more spectrum resources, the power consumption can be reduced by more than 70% using the Energy-Efficient Scheduling (EES) introduced in this paper.
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paper_title: Social serendipity: mobilizing social software

paper_content:

Many mobile devices incorporate low-power wireless connectivity protocols, such as Bluetooth, that can be used to identify an individual to other people nearby. We have developed an architecture that leverages this functionality in mobile phones - originally designed for communication at a distance - to connect people across the room. Serendipity is an application of the architecture. It combines the existing communications infrastructure with online introduction systems' functionality to facilitate interactions between physically proximate people through a centralized server. A new mobile-phone-based system uses Bluetooth hardware addresses and a database of user profiles to cue informal, face-to-face interactions between nearby users who don't know each other, but probably should.
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paper_title: Darwin phones: the evolution of sensing and inference on mobile phones

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We present Darwin, an enabling technology for mobile phone sensing that combines collaborative sensing and classification techniques to reason about human behavior and context on mobile phones. Darwin advances mobile phone sensing through the deployment of efficient but sophisticated machine learning techniques specifically designed to run directly on sensor-enabled mobile phones (i.e., smartphones). Darwin tackles three key sensing and inference challenges that are barriers to mass-scale adoption of mobile phone sensing applications: (i) the human-burden of training classifiers, (ii) the ability to perform reliably in different environments (e.g., indoor, outdoor) and (iii) the ability to scale to a large number of phones without jeopardizing the "phone experience" (e.g., usability and battery lifetime). Darwin is a collaborative reasoning framework built on three concepts: classifier/model evolution, model pooling, and collaborative inference. To the best of our knowledge Darwin is the first system that applies distributed machine learning techniques and collaborative inference concepts to mobile phones. We implement the Darwin system on the Nokia N97 and Apple iPhone. While Darwin represents a general framework applicable to a wide variety of emerging mobile sensing applications, we implement a speaker recognition application and an augmented reality application to evaluate the benefits of Darwin. We show experimental results from eight individuals carrying Nokia N97s and demonstrate that Darwin improves the reliability and scalability of the proof-of-concept speaker recognition application without additional burden to users.
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paper_title: Towards Safe Cities: A Mobile and Social Networking Approach

paper_content:

Population density and natural and man-made disasters make public safety a concern of growing importance. In this paper we aim to enable the vision of smart and safe cities by exploiting mobile and social networking technologies to securely and privately extract, model and embed real-time public safety information into quotidian user experiences. We first propose novel approaches to define location- and user-based safety metrics. We evaluate the ability of existing forecasting techniques to predict future safety values. We introduce iSafe, a privacy-preserving algorithm for computing safety snapshots of co-located mobile devices as well as geosocial network users. We present implementation details of iSafe as both an Android application and a browser plugin that visualizes safety levels of visited locations and browsed geosocial venues. We evaluate iSafe using crime and census data from the Miami-Dade (FL) county as well as data we collected from Yelp, a popular geosocial network.
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paper_title: How long to wait?: predicting bus arrival time with mobile phone based participatory sensing

paper_content:

The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.
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paper_title: Noisemap - real-time participatory noise maps

paper_content:

Noise pollution is a problem increasingly acknowledged ::: by authorities and governments around the globe. However, ::: creating noise maps with conventional methods is either inaccurate ::: or very expensive. To increase the spatial and temporal ::: data resolution a high number of sensors must be deployed. ::: In this paper we present results and prototypes based ::: on participatory sensing leading to accurate, real-time noise ::: maps. First we present NoiseMap a application currently ::: released for Android phones. NoiseMap gathers data on ::: loudness and transfers it to the open urban sensing platform ::: da sense. da sense allows users to access and control their ::: data, generate real-time noise maps and data graphs. Public ::: data is made available using either a web service or a ::: JavaScript API.
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paper_title: Efficient and flexible crowdsourcing of specialized tasks with precedence constraints

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Many companies now use crowdsourcing to leverage external (as well as internal) crowds to perform specialized work, and so methods of improving efficiency are critical. Tasks in crowdsourcing systems with specialized work have multiple steps and each step requires multiple skills. Steps may have different flexibilities in terms of obtaining service from one or multiple agents, due to varying levels of dependency among parts of steps. Steps of a task may have precedence constraints among them. Moreover, there are variations in loads of different types of tasks requiring different skill-sets and availabilities of different types of agents with different skill-sets. Considering these constraints together necessitates the design of novel schemes to allocate steps to agents. In addition, large crowdsourcing systems require allocation schemes that are simple, fast, decentralized and offer customers (task requesters) the freedom to choose agents. In this work we study the performance limits of such crowdsourcing systems and propose efficient allocation schemes that provably meet the performance limits under these additional requirements. We demonstrate our algorithms on data from a crowdsourcing platform run by a non-profit company and show significant improvements over current practice.
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paper_title: EEMC: Enabling Energy-Efficient Mobile Crowdsensing with Anonymous Participants

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Mobile Crowdsensing (MCS) requires users to be motivated to participate. However, concerns regarding energy consumption and privacy—among other things—may compromise their willingness to join such a crowd. Our preliminary observations and analysis of common MCS applications have shown that the data transfer in MCS applications may incur significant energy consumption due to the 3G connection setup. However, if data are transferred in parallel with a traditional phone call, then such transfer can be done almost “for free”: with only an insignificant additional amount of energy required to piggy-back the data—usually incoming task assignments and outgoing sensor results—on top of the call. Here, we present an Energy-Efficient Mobile Crowdsensing (EEMC) framework where task assignments and sensing results are transferred in parallel with phone calls. The main objective, and the principal contribution of this article, is an MCS task assignment scheme that guarantees that a minimum number of anonymous participants return sensor results within a specified time frame, while also minimizing the waste of energy due to redundant task assignments and considering privacy concerns of participants. Evaluations with a large-scale real-world phone call dataset show that our proposed EEMC framework outperforms the baseline approaches, and it can reduce overall energy consumption in data transfer by 54--66p when compared to the 3G-based solution.
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paper_title: PEIR, the personal environmental impact report, as a platform for participatory sensing systems research

paper_content:

PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.
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paper_title: Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype

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Imagers are an increasingly significant source of sensory observations about human activity and the urban environment. ImageScape is a software tool for processing, clustering, and browsing large sets of images. Implemented as a set of web services with an Adobe Flash-based user interface, it supports clustering by both image features and context tags, as well as re-tagging of images in the user interface. Though expected to be useful in many applications, ImageScape was designed as an analysis component of DietSense, a software system under development at UCLA to support (1) the use of mobile devices for automatic multimedia documentation of dietary choices with just-in-time annotation, (2) efficient post facto review of captured media by participants and researchers, and (3) easy authoring and dissemination of the automatic data collection protocols. A pilot study, in which participants ran software that enabled their phones to autonomously capture images of their plates during mealtime, was conducted using an early prototype of the DietSense system, and the resulting image set used in the creation of ImageScape. ImageScape will support two kinds of users within the DietSense application: The participants in dietary studies will have the ability to easily audit their images, while the recipients of the images, health care professionals managing studies and performing analysis, will be able to rapidly browse and annotate large sets of images.
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paper_title: A Robust Crowdsourcing-Based Indoor Localization System

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WiFi fingerprinting-based indoor localization has been widely used due to its simplicity and can be implemented on the smartphones. The major drawback of WiFi fingerprinting is that the radio map construction is very labor-intensive and time-consuming. Another drawback of WiFi fingerprinting is the Received Signal Strength (RSS) variance problem, caused by environmental changes and device diversity. RSS variance severely degrades the localization accuracy. In this paper, we propose a robust crowdsourcing-based indoor localization system (RCILS). RCILS can automatically construct the radio map using crowdsourcing data collected by smartphones. RCILS abstracts the indoor map as the semantics graph in which the edges are the possible user paths and the vertexes are the location where users may take special activities. RCILS extracts the activity sequence contained in the trajectories by activity detection and pedestrian dead-reckoning. Based on the semantics graph and activity sequence, crowdsourcing trajectories can be located and a radio map is constructed based on the localization results. For the RSS variance problem, RCILS uses the trajectory fingerprint model for indoor localization. During online localization, RCILS obtains an RSS sequence and realizes localization by matching the RSS sequence with the radio map. To evaluate RCILS, we apply RCILS in an office building. Experiment results demonstrate the efficiency and robustness of RCILS.
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paper_title: Learning-Based Energy-Efficient Data Collection by Unmanned Vehicles in Smart Cities

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Mobile crowdsourcing (MCS) is now an important source of information for smart cities, especially with the help of unmanned aerial vehicles (UAVs) and driverless cars. They are equipped with different kinds of high-precision sensors, and can be scheduled/controlled completely during data collection, which will make MCS system more robust. However, they are limited to energy constraint, especially for long-term, long-distance sensing tasks, and cities are almost too crowded to set stationary charging station. Towards this end, in this paper we propose to leverage emerging deep reinforcement learning (DRL) techniques for enabling model-free unmanned vehicles control, and present a novel and highly effective control framework, called “DRL-RVC.” It utilizes the powerful convolutional neural network for feature extraction of the necessary information (including sample distribution, traffic flow, etc.), then makes decisions under the guidance of the deep Q network. That is, UAVs will cruise in the city without control and collect most required data in the sensing region, while mobile unmanned charging station will reach the charging point in the shortest possible time. Finally, we validate and evaluate the proposed framework via extensive simulations based on a real dataset in Rome. Extensive simulation results well justify the effectiveness and robustness of our approach.
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paper_title: WreckWatch : Automatic Traffic Accident Detection and Notification with Smartphones

paper_content:

Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles. This paper describes how smartphones, such as the iPhone and Google Android platforms, can automatically detect traffic accidents using accelerometers and acoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coordinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that combines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awareness to first responders, and (3) we provide empirical results demonstrating the efficacy of different approaches employed by smartphone accident detection systems to prevent false positives.
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paper_title: VTrack: accurate, energy-aware road traffic delay estimation using mobile phones

paper_content:

Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors---GPS, WiFi, and/or cellular triangulation---opens up the attractive possibility of using position samples from drivers' phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don't have GPS at all, the GPS sensor doesn't work in "urban canyons" (tall buildings and tunnels) or when the phone is inside a pocket, and the GPS on many phones is power-hungry and drains the battery quickly. In these cases, VTrack can use alternative, less energy-hungry but noisier sensors like WiFi to estimate both a user's trajectory and travel time along the route. VTrack uses a hidden Markov model (HMM)-based map matching scheme and travel time estimation method that interpolates sparse data to identify the most probable road segments driven by the user and to attribute travel times to those segments. We present experimental results from real drive data and WiFi access point sightings gathered from a deployment on several cars. We show that VTrack can tolerate significant noise and outages in these location estimates, and still successfully identify delay-prone segments, and provide accurate enough delays for delay-aware routing algorithms. We also study the best sampling strategies for WiFi and GPS sensors for different energy cost regimes.
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paper_title: EmotionSense: a mobile phones based adaptive platform for experimental social psychology research

paper_content:

Today's mobile phones represent a rich and powerful computing platform, given their sensing, processing and communication capabilities. Phones are also part of the everyday life of billions of people, and therefore represent an exceptionally suitable tool for conducting social and psychological experiments in an unobtrusive way. de the ability of sensing individual emotions as well as activities, verbal and proximity interactions among members of social groups. Moreover, the system is programmable by means of a declarative language that can be used to express adaptive rules to improve power saving. We evaluate a system prototype on Nokia Symbian phones by means of several small-scale experiments aimed at testing performance in terms of accuracy and power consumption. Finally, we present the results of real deployment where we study participants emotions and interactions. We cross-validate our measurements with the results obtained through questionnaires filled by the users, and the results presented in social psychological studies using traditional methods. In particular, we show how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.
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paper_title: Distributed Data Collection Control in Opportunistic Mobile Crowdsensing

paper_content:

Many researchers have nowadays shown a paramount interest in the rising field of Mobile Crowdsensing (MCS). Such paradigm is considered an easy and cost-effective choice for observing phenomena of common interest within the scope of Smart Cities and environmental monitoring. Nevertheless, it brings along many issues, such as fostering participation, reducing the power consumption of end devices and granting coverage. In this paper we focus on the problem of data collection control, which aims to avoid data redundancy and useless power consuming data transfers while assuring a sufficient number of observations for the purpose of coverage. In particular, we design a probabilistic distributed algorithm that aims to achieve a total per-zone number of observations close to a defined amount, while maximizing the fairness among users. We provide both the analytical definition of our algorithm and the performance evaluation through extensive simulations, establishing our algorithm as a good baseline for a poorly investigated problem.
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paper_title: Non-Interactive Privacy-Preserving Truth Discovery in Crowd Sensing Applications

paper_content:

In crowd sensing, truth discovery (TD) refers to finding reliable information from noisy/biased data collected from different providers. To protect providers' data while enabling truth distillation, privacy-preserving truth discovery (PPTD) has received wide attention recently. However, all existing approaches require iterative interaction between server(s) and individual providers, which inevitably demand all providers to be always online. Otherwise, the protocol would fail or expose extra provider information. In this paper, we design and implement the first non-interactive PPTD system that completely removes the online requirement with strong privacy guarantees. Our framework follows the same two-server model from the best-known prior solution, and leverages Yao's Garbled Circuit (GC). Yet, we devise non-trivial speedup techniques for TD-optimized implementation. Firstly, we identify reusable computations in TD to accelerate the circuit generation. Secondly, we securely evaluate the burdensome non-linear functions in TD via customized approximation with accuracy and improved efficiency. Thirdly, we reduce the online execution time by bridging together latest advancements of component-based GC and various computations needed in TD. Unlike prior arts, our framework does not reveal any intermediate results, and further supports “late-join” providers without protocol suspension/restart. The practical performance of our proof-of-concept implementation is verified through extensive evaluations.
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paper_title: Participatory Air Pollution Monitoring Using Smartphones

paper_content:

Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on o-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the eect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.
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paper_title: Crowdsourcing of Pollution Data using Smartphones

paper_content:

In this paper we present our research into participatory sensing based solutions for the collection of data on urban pollution and nuisance. In the past 2 years we have been involved in the NoiseTube project which explores a crowdsourcing approach to measuring and mapping urban noise pollution using smartphones. By involving the general public and using off-the-shelf smartphones as noise sensors, we seek to provide a low cost solution for citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community. We believe our work represents an interesting example of the novel mobile crowdsourcing applications which are enabled by ubiquitous computing systems. Furthermore we believe the NoiseTube system, and the currently ongoing validation experiments, provide an illustrative context for some of the open challenges faced by creators of ubiquitous crowdsourcing applications and services in general. We will also take the opportunity to present the insights we gained into some of the challenges.
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paper_title: Sybil-Proof Online Incentive Mechanisms for Crowdsensing

paper_content:

Crowdsensing leverages the rapid growth of sensor-embedded smartphones and human mobility for pervasive information collection. To incentivize smartphone users to participate in crowdsensing, many auction-based incentive mechanisms have been proposed for both offline and online scenarios. It has been demonstrated that the Sybil attack may undermine these mechanisms. In a Sybil attack, a user illegitimately pretends multiple identities to gain benefits. Sybil-proof incentive mechanisms have been proposed for the offline scenario. However, the problem of designing Sybil-proof online incentive mechanisms for crowdsensing is still open. Compared to the offline scenario, the online scenario provides users one more dimension of flexibility, i.e., active time, to conduct Sybil attacks, which makes this problem more challenging. In this paper, we design Sybil-proof online incentive mechanisms to deter the Sybil attack for crowdsensing. Depending on users' flexibility on performing their tasks, we investigate both single-minded and multi-minded cases and propose SOS and SOM, respectively. SOS achieves computational efficiency, individual rationality, truthfulness, and Sybil-proofness. SOM achieves individual rationality, truthfulness, and Sybil-proofness. Through extensive simulations, we evaluate the performance of SOS and SOM.
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paper_title: Does the Configuration of the Street Network Influence Where Outdoor Serious Violence Takes Place? Using Space Syntax to Test Crime Pattern Theory

paper_content:

Objectives ::: To examine the effect of the physical layout of the street network on the spatial distribution of outdoor serious violence. Crime pattern theory predicts crime would be more prevalent on more connected, accessible or traveled street segments, as these will be more likely to fall within an offender’s awareness space.
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paper_title: Crowdsensing Framework for Monitoring Bridge Vibrations Using Moving Smartphones

paper_content:

Cities are encountering extensive deficits in infrastructure service while they are experiencing rapid technological advancements and overhauls in transportation systems. Standard bridge evaluation methods rely on visual inspections, which are infrequent and subjective, ultimately affecting the structural assessments on which maintenance plans are based. The operational behavior of a bridge must be observed more regularly and over an extended period in order to sufficiently track its condition and avoid unexpected rehabilitation. Mobile sensor networks are conducive to monitoring bridges vibrations routinely, with benefits that have been demonstrated in recent structural health monitoring (SHM) research. Though smartphone accelerometers are imperfect sensors, they can contribute valuable information to SHM, especially when aggregated, e.g., via crowdsourcing. In an application on the Harvard Bridge (Boston, MA), it is shown that acceleration data collected using smartphones in moving vehicles contained consistent and significant indicators of the first three modal frequencies of the bridge. In particular, the results became more precise when informatics from several smartphone datasets were combined. This evidence is the first to support the hypothesis that smartphone data, collected within vehicles passing over a bridge, can be used to detect several modal frequencies of the bridge. The result defines an opportunity for local governments to make partnerships that encourage the collection of low-cost bridge vibration data, which can contribute to more effective management and informed decision-making.
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paper_title: How many people are needed for a crowdsensing campaign?

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Environmental scientists, city planners, park administrators, and similar others, need to base their decisions on up-to-date, accurate and high resolution measurement data. However, acquiring such data by hand or by deploying sensor networks is expensive and time consuming. People without proper scientific training want to take part in the scientific process and assist with the needs of their respective communities. Crowdsensing is a technology-driven area where ICT platforms are being developed which permit anyone to participate in processes that help expand our understanding and improve our surroundings. Crowdsensing refers to the process in which crowds (large number of people) measure specific features and share the resulting data or send it to a central location in which it can be used by the people that need it.
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paper_title: Automatic Collection of Fuel Prices from a Network of Mobile Cameras

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It is an undeniable fact that people want information. Unfortunately, even in today's highly automated society, a lot of the information we desire is still manually collected. An example is fuel prices where websites providing fuel price information either send their workers out to manually collect the prices or depend on volunteers manually relaying the information. This paper proposes a novel application of wireless sensor networks to automatically collect fuel prices from camera images of road-side price board (billboard) of service (or gas) stations. Our system exploits the ubiquity of mobile phones that have cameras as well as users contributing and sharing data. In our proposed system, cameras of contributing users will be automatically triggered when they get close to a service station. These images will then be processed by computer vision algorithms to extract the fuel prices. In this paper, we will describe the system architecture and present results from our computer vision algorithms. Based on 52 images, our system achieves a hit rate of 92.3% for correctly detecting the fuel price board from the image background and reads the prices correctly in 87.7% of them. To the best of our knowledge, this is the first instance of a sensor network being used for collecting consumer pricing information.
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paper_title: HazeWatch: A participatory sensor system for monitoring air pollution in Sydney

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This paper describes our experiences with building and deploying a low-cost participatory system for urban air pollution monitoring in Sydney. Though air pollution imposes significant health costs on the urban community globally, today's published data on pollution concentrations is spatially too sparse, and does not allow for sufficiently accurate estimation of exposures for (potentially mobile) individuals in order to make medical inferences. The HazeWatch project described in this paper uses several low-cost mobile sensor units attached to vehicles to measure air pollution concentrations, and users' mobile phones to tag and upload the data in real time. The greater spatial granularity of data thus collected enables creation of pollution maps of metropolitan Sydney viewable in real-time over the web, as well as personalized apps that show the individual's exposure history and allow for route planning to reduce future exposure. We share the insights we obtained from building and trialling such a system in Sydney, and highlight challenges that can be addressed collaboratively by groups developing similar systems world-wide.
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paper_title: WasteApp: Smarter waste recycling for smart citizens

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Waste recycling and disposal is an increasingly pressing issue for the preservation of the Earth natural environment. A considerable amount of research has been carried on factors influencing household waste recycling behaviors and a plethora of supporting tools have been investigated in the last few years. Despite this, recycling waste is still perceived as a cumbersome task, and people around the world are often struggling to find efficient ways to recycle their waste. We argue that, among many factors, a lack of user centered design in waste recycling support tools can be one of the causes. For such reason, we approach the design of a new waste recycling support application, the “WasteApp”, by merging behavioral studies and common features of existing mobile apps with a co-design methodology. We present the outcomes of several co-design sessions aimed at gathering real user needs for waste recycling, an initial design of the “WasteApp” and we discuss the results of a preliminary small-scale user study on the application usability.
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paper_title: Creek watch: pairing usefulness and usability for successful citizen science

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Citizen science projects can collect a wealth of scientific data, but that data is only helpful if it is actually used. While previous citizen science research has mostly focused on designing effective capture interfaces and incentive mechanisms, in this paper we explore the application of HCI methods to ensure that the data itself is useful. To provide a focus for this exploration we designed and implemented Creek Watch, an iPhone application and website that allow volunteers to report information about waterways in order to aid water management programs. Working with state and local officials and private groups involved in water monitoring, we conducted a series of contextual inquiries to uncover what data they wanted, what data they could immediately use, and how to most effectively deliver that data to them. We iteratively developed the Creek Watch application and website based on our findings and conducted evaluations of it with both contributors and consumers of water data, including scientists at the city water resources department. Our study reveals that the data collected is indeed useful for their existing practices and is already in use in water and trash management programs. Our results suggest the application of HCI methods to design the data for the end users is just as important as their use in designing the user interface.
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paper_title: MobiBee: a mobile treasure hunt game for location-dependent fingerprint collection

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Constructing a location-dependent fingerprint map is one of the essential steps for fingerprint-based indoor localization. A comprehensive site survey is time-consuming, labor-intensive, and subjects to environmental changes. In this work, we develop a mobile participatory game, MobiBee, to collect fingerprints with the help of quick response (QR) codes (posted on walls or pillars as location tags). Various incentive strategies, including monetary, entertainment and competition, are utilized. The location marker is deliberately designed to ensure that fingerprints are collected at the targeted locations in a stationary manner. Interestingly, a few instances of QR code forgery have been identified during the game play, which exposes the vulnerability of using QR code in indoor localization. A fraud detection mechanism is hence proposed. Experiments show that the proposed approach can accurately detect the QR code forgery and improve indoor localization accuracy.
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paper_title: Behavior-aware UAV-assisted crowd sensing technique for urban vehicular environments

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Measuring vehicles density and distribution in urban environments is an important task. The results of such estimation are highly required for different applications such as road lights configuration, congestion control, and also inter-vehicle data routing. This task, which is known as crowd sensing, is mostly based on smartphone-assisted sensing. However, in urban environments the multiple kinds of obstacles make it hard and mostly inaccurate especially for RoadSide Units (RSUs) low density cases. Furthermore, the assumption that all vehicles are collaborative and honest can lead to unexpected and unwanted situations. To address the above mentioned problems, we propose in this paper a trust-aware crowd sensing technique based on Unmanned Aerial Vehicle (UAV) for vehicular urban environments. Considering the real traffic information and the distribution of dishonest nodes in the network gathered by UAVs, our proposed solution provides a global view to both vehicles and RSUs, which can be used for different applications such as: finding the shortest and most trusted possible path to messages' final destinations, and also for the intelligent congestion control. Our simulation results show that our solution offers instant crowd and trust information over which in addition to the high detection ratios, also high packet delivery ratios with low network overhead are achieved.
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paper_title: SoundSense: scalable sound sensing for people-centric applications on mobile phones

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Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sensing applications. Perhaps the most ubiquitous and unexploited sensor on mobile phones is the microphone - a powerful sensor that is capable of making sophisticated inferences about human activity, location, and social events from sound. In this paper, we exploit this untapped sensor not in the context of human communications but as an enabler of new sensing applications. We propose SoundSense, a scalable framework for modeling sound events on mobile phones. SoundSense is implemented on the Apple iPhone and represents the first general purpose sound sensing system specifically designed to work on resource limited phones. The architecture and algorithms are designed for scalability and Soundsense uses a combination of supervised and unsupervised learning techniques to classify both general sound types (e.g., music, voice) and discover novel sound events specific to individual users. The system runs solely on the mobile phone with no back-end interactions. Through implementation and evaluation of two proof of concept people-centric sensing applications, we demostrate that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives.
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paper_title: Mobile Social Networks: Architectures, Social Properties, and Key Research Challenges

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Mobile social networks (MSNs) are mobile communication systems focusing not only on the behaviour but also on the social needs of the users. In a broader view all mobile systems used by people in their everyday lives can be characterized as MSNs, since all interactions taking place follow social rather than random patterns. Whether the deployed communication system takes into account the social background of the underlying network, depends on its form and capabilities. This article presents a review of the relevant work published in MSNs. Initially the basic architectures and components are summarized. Then the basic social properties of the network, as found in the key literature, are extensively examined. These properties are the main source of inspiration for new MSN protocols and applications, especially for non centralized systems. Finally the key research problems and the open issues in the area are presented, including future applications and privacy concerns.
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paper_title: $QnQ$ Q n Q : Quality and Quantity Based Unified Approach for Secure and Trustworthy Mobile Crowdsensing

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A major challenge in mobile crowdsensing applications is the generation of false (or spam) contributions resulting from selfish and malicious behaviors of users, or wrong perception of an event. Such false contributions induce loss of revenue owing to undue incentivization, and also affect the operational reliability of the applications. To counter these problems, we propose an event-trust and user-reputation model, called $QnQ$ Q n Q , to segregate different user classes such as honest, selfish, or malicious. The resultant user reputation scores, are based on both ‘quality’ (accuracy of contribution) and ‘quantity’ (degree of participation) of their contributions. Specifically, $QnQ$ Q n Q exploits a rating feedback mechanism for evaluating an event-specific expected truthfulness, which is then transformed into a robust quality of information (QoI) metric to weaken various effects of selfish and malicious user behaviors. Eventually, the QoIs of various events in which a user has participated are aggregated to compute his reputation score, which in turn is used to judiciously disburse user incentives with a goal to reduce the incentive losses of the CS application provider. Subsequently, inspired by cumulative prospect theory (CPT) , we propose a risk tolerance and reputation aware trustworthy decision making scheme to determine whether an event should be published or not, thus improving the operational reliability of the application. To evaluate $QnQ$ Q n Q experimentally, we consider a vehicular crowdsensing application as a proof-of-concept. We compare QoI performance achieved by our model with Josang's belief model, reputation scoring with Dempster-Shafer based reputation model, and operational (decision) accuracy with expected utility theory. Experimental results demonstrate that $QnQ$ Q n Q is able to better capture subtle differences in user behaviors based on both quality and quantity, reduces incentive losses, and significantly improves operational accuracy in presence of rogue contributions.
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paper_title: SafeStreet: An automated road anomaly detection and early-warning system using mobile crowdsensing

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In this work, we present SafeStreet, an automated road anomaly detection and early warning system. A pilot of SafeStreet was conducted by deploying the SafeStreet mobile app on 1800 cabs in Mumbai and Bengaluru. We discuss some of the design considerations and early results from our pilot study.
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paper_title: When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning

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With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, UAV-aided MCS allows autonomous data collection anytime and anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we consider the fixed-wing UAV-aided MCS system and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on either dynamic programming or genetic algorithms, and the task assignment problem is addressed in the second stage by exploring the Gale–Shapley algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.
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paper_title: WhozThat? evolving an ecosystem for context-aware mobile social networks

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One of the most compelling social questions, which until now was left unanswered by current technology, is "Who's that?" This question is usually asked about a new unfamiliar person in a social setting who piques your curiosity. This article presents WhozThat, a system that ties together online social networks with mobile smart- phones to answer this common and essential social question. WhozThat builds an infrastructure that shares social networking IDs locally, using wireless technology, while also leveraging a wireless connection to the Internet's social networks to bind identity with location. In addition, WhozThat offers an entire ecosystem on which increasingly complex context-aware applications can be built; that is, once the environment knows who one is, the environment can adapt its content based on the individual's identity or even the collective tastes of a local group. We describe a prototype of the basic WhozThat system and also describe a more advanced service we built, a context-aware music player. We also discuss security and privacy issues introduced by mobile social networks.
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paper_title: Automatically characterizing places with opportunistic crowdsensing using smartphones

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Automated and scalable approaches for understanding the semantics of places are critical to improving both existing and emerging mobile services. In this paper, we present CrowdSense@Place (CSP), a framework that exploits a previously untapped resource -- opportunistically captured images and audio clips from smartphones -- to link place visits with place categories (e.g., store, restaurant). CSP combines signals based on location and user trajectories (using WiFi/GPS) along with various visual and audio place "hints" mined from opportunistic sensor data. Place hints include words spoken by people, text written on signs or objects recognized in the environment. We evaluate CSP with a seven-week, 36-user experiment involving 1,241 places in five locations around the world. Our results show that CSP can classify places into a variety of categories with an overall accuracy of 69%, outperforming currently available alternative solutions.
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paper_title: Piggyback CrowdSensing (PCS): energy efficient crowdsourcing of mobile sensor data by exploiting smartphone app opportunities

paper_content:

Fueled by the widespread adoption of sensor-enabled smartphones, mobile crowdsourcing is an area of rapid innovation. Many crowd-powered sensor systems are now part of our daily life -- for example, providing highway congestion information. However, participation in these systems can easily expose users to a significant drain on already limited mobile battery resources. For instance, the energy burden of sampling certain sensors (such as WiFi or GPS) can quickly accumulate to levels users are unwilling to bear. Crowd system designers must minimize the negative energy side-effects of participation if they are to acquire and maintain large-scale user populations. To address this challenge, we propose Piggyback CrowdSensing (PCS), a system for collecting mobile sensor data from smartphones that lowers the energy overhead of user participation. Our approach is to collect sensor data by exploiting Smartphone App Opportunities -- that is, those times when smartphone users place phone calls or use applications. In these situations, the energy needed to sense is lowered because the phone need no longer be woken from an idle sleep state just to collect data. Similar savings are also possible when the phone either performs local sensor computation or uploads the data to the cloud. To efficiently use these sporadic opportunities, PCS builds a lightweight, user-specific prediction model of smartphone app usage. PCS uses this model to drive a decision engine that lets the smartphone locally decide which app opportunities to exploit based on expected energy/quality trade-offs. We evaluate PCS by analyzing a large-scale dataset (containing 1,320 smartphone users) and building an end-to-end crowdsourcing application that constructs an indoor WiFi localization database. Our findings show that PCS can effectively collect large-scale mobile sensor datasets (e.g., accelerometer, GPS, audio, image) from users while using less energy (up to 90% depending on the scenario) compared to a representative collection of existing approaches.
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paper_title: Ensuring energy efficient coverage for participatory sensing in urban streets

paper_content:

Participatory sensing is an approach to data collection for monitoring different scenarios with the help of smartphone sensors. As more and more sensors are being added to smartphones, monitoring a wide range of scenarios has become possible with participatory sensing. An important issue in such participatory sensing application is the coverage of the collected data that reflects how well the data samples represent the monitored area. In the traditional approach, the data collection process is assisted by a server that knows the location of the participating devices and selects the necessary ones to cover the monitored area efficiently. However, for battery powered devices like smartphones, sending frequent location updates to the server is quite energy expensive. In this paper, we propose a framework, called STREET, for data collection from urban streets that can address the coverage problem where a participating mobile device is not required to send location updates to the server. In particular, our framework can collect data samples to ensure the requirements of a specified partial coverage, full coverage and k-coverage. STREET is assisted by a simple localization scheme for mobile devices that minimizes the usage of location sensor (e.g., GPS) while participating in the data collection process. Experiments from simulation studies show that our approach can significantly reduce energy consumption of the participating mobile devices.
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paper_title: MobiClique: middleware for mobile social networking

paper_content:

We consider a mobile ad hoc network setting where Bluetooth enabled mobile devices communicate directly with other devices as they meet opportunistically. We design and implement a novel mobile social networking middleware named MobiClique. MobiClique forms and exploits ad hoc social networks to disseminate content using a store-carry-forward technique. Our approach distinguishes itself from other mobile social software by removing the need for a central server to conduct exchanges, by leveraging existing social networks to bootstrap the system, and by taking advantage of the social network overlay to disseminate content. We also propose an open API to encourage third-party application development. We discuss the system architecture and three example applications. We show experimentally that MobiClique successfully builds and maintains an ad hoc social network leveraging contact opportunities between friends and people sharing interest(s) for content exchanges. Our experience also provides insight into some of the key challenges and short-comings that researchers face when designing and deploying similar systems.
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paper_title: MCNet: Crowdsourcing wireless performance measurements through the eyes of mobile devices

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Measurement of network performance in complex WiFi networks, such as in corporations and universities, is an important but challenging task, as wireless performance in networks with many access points is hard to measure and model. We demonstrate that crowdsourcing the task of measuring WiFi performance is an effective solution to this problem. By measuring performance directly with unmodified consumer mobile devices such as smartphones, it is possible to cheaply and easily detect problems that matter to users. Aggregated performance data across clients can then provide a global view of performance trends in an enterprise network. We demonstrate that periodic sampling allows the collection of representative data while keeping battery consumption low, and we leverage mobile sensor information to intelligently schedule these measurements. This system was deployed in two different large WLANs, where we discovered numerous previously undetected performance problems.
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paper_title: A Location-Based Mobile Crowdsensing Framework Supporting a Massive Ad Hoc Social Network Environment

paper_content:

This article addresses one of the key challenges of engaging a massive ad hoc crowd by providing sustainable incentives. The incentive model is based on a context-aware cyber-physical spatio-temporal serious game with the help of a mobile crowd sensing mechanism. To this end, this article describes a framework that can create an ad hoc social network of millions of people and provide context-aware serious-game services as an incentive. While interacting with different services, the massive crowd shares a rich trail of geo-tagged multimedia data, which acts as a crowdsourcing eco-system. The incentive model has been tested on the mass crowd at the Hajj since 2014. From our observations, we conclude that the framework provides a sustainable incentive mechanism that can solve many real-life problems such as reaching a person in a crowd within the shortest possible time, isolating significant events, finding lost individuals, handling emergency situations, helping pilgrims to perform ritual events based on location and time, and sharing geo-tagged multimedia resources among a community of interest within the crowd. The framework allows an ad hoc social network to be formed within a very large crowd, a community of interests to be created for each person, and information to be shared with the right community of interests. We present the communication paradigm of the framework, the serious game incentive model, and cloud-based massive geo-tagged social network architecture.
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paper_title: Understanding the coverage and scalability of place-centric crowdsensing

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Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications.
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paper_title: Mobile Crowd-Sensing Wireless Activity with Measured Interference Power

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In this paper, we present a novel method for sensing the volume of wireless throughput of other users by measuring the passive interference power received on mobile devices. The benefit of this mobile crowd-sensing approach is that it offers a non-intrusive way of inferring the level of wireless traffic, without requiring to extract data from commercial or private devices. The availability of cross-network wireless traffic data can allow individual operators to deploy femto-cells effectively. The proposed technique requires only approximate location data and is independent of the traffic pattern. The results show that the sensing accuracy for cell-level activity is high (<; 5% error). The paper further demonstrates how this novel method can allow researchers to rapidly build up a map of the wireless activity level in a large region using crowd-sensing.
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paper_title: Lowering the barriers to large-scale mobile crowdsensing

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Mobile crowdsensing is becoming a vital technique for environment monitoring, infrastructure management, and social computing. However, deploying mobile crowdsensing applications in large-scale environments is not a trivial task. It creates a tremendous burden on application developers as well as mobile users. In this paper we try to reveal the barriers hampering the scale-up of mobile crowdsensing applications, and to offer our initial thoughts on the potential solutions to lowering the barriers.
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paper_title: Detecting Location Fraud in Indoor Mobile Crowdsensing

paper_content:

Mobile crowdsensing allows a large number of mobile devices to measure phenomena of common interests and form a body of knowledge about natural and social environments. In order to get location annotations for indoor mobile crowdsensing, reference tags are usually deployed which are susceptible to tampering and compromises by attackers. In this work, we consider three types of location-related attacks including tag forgery, tag misplacement, and tag removal. Different detection algorithms are proposed to deal with these attacks. First, we introduce location-dependent fingerprints as supplementary information for better location identification. A truth discovery algorithm is then proposed to detect falsified data. Moreover, visiting patterns are utilized for the detection of tag misplacement and removal. Experiments on both crowdsensed and emulated dataset show that the proposed algorithms can detect all three types of attacks with high accuracy.
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paper_title: Sensing, communication and security planes: A new challenge for a smart city system design

paper_content:

Abstract The Internet of Things (IoT) era is evolving into sensor initiated, actuation-driven, and machine intelligence-based decision making platform for smart cities. A smart city system aims at a seamless and secure interconnection of sensors, actuators, and data processing resources to ensure digital, efficient, and reliable services. In this article, we present a brief planar overview of a smart city system architecture by introducing the application, sensing, communication, data, and security/privacy planes. Tailoring existing communication protocols and infrastructures to bridge massively deployed sensors and data processing/storage resources introduces unique communication challenges for smart cities. Furthermore, co-existence, integration, and control of dedicated and non-dedicated sensors is a grand challenge while IoT sensors continuously push sensory data through the communication medium towards data processing and analysis planes. While pervasiveness and ubiquity of smart city services are ensured by the interaction of communication and sensing technologies, their robustness and resilience call for customized security and privacy solutions. With these in mind, we focus on sensing/actuation, communication, and security planes of a smart city system and present a comprehensive survey of the challenges and state-of-the-art solutions in each plane. Furthermore, we provide insights for open issues and opportunities in these planes.
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paper_title: ShareLikesCrowd: Mobile analytics for participatory sensing and crowd-sourcing applications

paper_content:

Data and continuous data streams from mobile users/devices are becoming increasingly important for numerous applications including urban modelling, transportation, and more recently for mobile crowd-sensing to support citizen journalism and participatory sensing where sensor informatics and social networking meet. While significant efforts have focused towards the analysis of mobile user data, a key challenge that needs to be addressed in order to realize the full-potential is to address the scalability issues of real-time data collection and processing at run time. By scalability, we refer to both the challenges of data capture from a large number of users, as well as the issues of energy consumed on individual devices as a result of that capture. In this paper, we present mobile/on-board data stream mining as an effective approach to address the scalability issues of mobile data collection and run-time processing and as a significant component of mobile run-time analytics. We present experimental evaluation using the Nokia mobile data challenge open track dataset to show the significant energy and bandwidth savings that mobile data stream mining can achieve with no significant loss of useful information in this process.
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paper_title: Retrofitting smartphones to be used as particulate matter dosimeters

paper_content:

This work discusses ways of measuring particulate matter with mobile devices. Solutions using a dedicated sensor device are presented along with a novel method of retrofitting a sensor to a camera phone without need for electrical modifications. Instead, the flash and camera of the phone are used as light source and receptor of an optical dust sensor respectively. Experiments to evaluate the accuracy are presented.
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paper_title: Secret-sharing-based secure user recruitment protocol for mobile crowdsensing

paper_content:

Mobile crowdsensing is a new paradigm in which a requester can recruit a group of mobile users via a platform and coordinate them to perform some sensing tasks by using their smartphones. In mobile crowdsensing, each user might perform multiple tasks with different sensing qualities. An important problem is recruiting the minimum number of users while achieving a satisfactory sensing quality for each task. Meanwhile, in order to ease users' worries about privacy disclosures, the user recruitment process needs to protect each user's sensing quality information from being revealed to other users or to the platform. We prove that this problem is NP-hard. To solve this problem, we first propose a Basic User Recruitment (BUR) protocol based on a greedy strategy, which can recruit nearly the minimum amount of users while ensuring that the total sensing quality of each task is no less than a given threshold. Based on BUR, we further propose a Secure User Recruitment (SUR) protocol by using secret sharing schemes. We analyze the approximation ratio and prove the security of the SUR protocol in the semi-honest model. Moreover, we extend SUR to deal with a more general case where the total sensing quality of each task might be an increasing submodular function. Finally, we demonstrate the significant performance of the proposed protocol through extensive simulations and execution in real smartphones.
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paper_title: MoVi: mobile phone based video highlights via collaborative sensing

paper_content:

Sensor networks have been conventionally defined as a network of sensor motes that collaboratively detect events and report them to a remote monitoring station. This paper makes an attempt to extend this notion to the social context by using mobile phones as replacement of motes. We envision a social application where mobile phones collaboratively sense their ambience and recognize socially "interesting" events. The phone with a good view of the event triggers a video recording, and later, the video-clips from different phones are "stitched" into a video highlights of the occasion. We observe that such a video highlights is akin to the notion of event coverage in conventional sensor networks, only the notion of "event" has changed from physical to social. We have built a Mobile Phone based Video Highlights system (MoVi) using Nokia phones and iPod Nanos, and have experimented in real-life social gatherings. Results show that MoVi-generated video highlights (created offline) are quite similar to those created manually, (i.e., by painstakingly editing the entire video of the occasion). In that sense, MoVi can be viewed as a collaborative information distillation tool capable of filtering events of social relevance.
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paper_title: A Review on Internet of Things for Defense and Public Safety

paper_content:

The Internet of Things (IoT) is undeniably transforming the way that organizations communicate and organize everyday businesses and industrial procedures. Its adoption has proven well suited for sectors that manage a large number of assets and coordinate complex and distributed processes. This survey analyzes the great potential for applying IoT technologies (i.e., data-driven applications or embedded automation and intelligent adaptive systems) to revolutionize modern warfare and provide beneﬁts similar to those in industry. It identiﬁes scenarios where Defense and Public Safety (PS) could leverage better commercial IoT capabilities to deliver greater survivability to the warﬁghter or ﬁrst responders, while reducing costs and increasing operation efﬁciency and effectiveness. This article reviews the main tactical requirements and the architecture, examining gaps and shortcomings in existing IoT systems across the military ﬁeld and mission-critical scenarios. The review characterizes the open challenges for a broad deployment and presents a research roadmap for enabling an affordable IoT for defense and PS.
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paper_title: SPA: a smart phone assisted chronic illness self-management system with participatory sensing

paper_content:

The medical system has not been able to effectively adapt to the dramatic transformation in public health challenges; from acute to chronic and lifestyle-related illnesses. Although acute illnesses can be treated successfully in an office or hospital, chronic illnesses comprise the bulk of health care needs and require a very different approach. There is overwhelming consensus that the prevention and treatment of cardiovascular disease, diabetes, hypertension, chronic pain, obesity, asthma, HIV, and many other chronic illnesses require substantial patient self-management [2].
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paper_title: SociableSense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing

paper_content:

The interactions and social relations among users in workplaces have been studied by many generations of social psychologists. There is evidence that groups of users that interact more in workplaces are more productive. However, it is still hard for social scientists to capture fine-grained data about phenomena of this kind and to find the right means to facilitate interaction. It is also difficult for users to keep track of their level of sociability with colleagues. While mobile phones offer a fantastic platform for harvesting long term and fine grained data, they also pose challenges: battery power is limited and needs to be traded-off for sensor reading accuracy and data transmission, while energy costs in processing computationally intensive tasks are high. In this paper, we propose SociableSense, a smart phones based platform that captures user behavior in office environments, while providing the users with a quantitative measure of their sociability and that of colleagues. We tackle the technical challenges of building such a tool: the system provides an adaptive sampling mechanism as well as models to decide whether to perform computation of tasks, such as the execution of classification and inference algorithms, locally or remotely. We perform several micro-benchmark tests to fine-tune and evaluate the performance of these mechanisms and we show that the adaptive sampling and computation distribution schemes balance trade-offs among accuracy, energy, latency, and data traffic. Finally, by means of a social psychological study with ten participants for two working weeks, we demonstrate that SociableSense fosters interactions among the participants and helps in enhancing their sociability.
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paper_title: Evaluating the Reliability of Phones as Seismic Monitoring Instruments

paper_content:

Emergency responders must “see” the effects of an earthquake clearly and rapidly for effective response. This paper presents a novel use of cell phone and information technology to measure ground motion intensity parameters. The phone sensor is an imperfect device and has a limited operational range. Thus, shake table tests were performed to evaluate their reliability as seismic monitoring instruments. Representative handheld devices, either rigidly connected to the table or free to move, measured shaking intensity parameters well. Bias in 5%-damped spectral accelerations measured by phones was less than 0.05 and 0.2 [log(g)] during one-dimensional (1-D) and three-dimensional (3-D) shaking in frequencies ranging from 1 Hz to 10 Hz. They did tend to overestimate the Arias Intensity, but this error declined for stronger motions with larger signal-to-noise ratios. With these ubiquitous measurement devices, a more accurate and rapid portrayal of the damage distribution during an earthquake can be provided.
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paper_title: Ear-phone: an end-to-end participatory urban noise mapping system

paper_content:

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end participatory urban noise mapping system called Ear-Phone. Ear-Phone, for the first time, leverages Compressive Sensing to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95 and HP iPAQ mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. Extensive simulations and outdoor experiments demonstrate that Ear-Phone is a feasible platform to assess noise pollution, incurring reasonable system resource consumption at mobile devices and providing high reconstruction accuracy of the noise map.
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paper_title: Mobile Phones as Seismologic Sensors: Automating Data Extraction for the iShake System

paper_content:

There are a variety of approaches to seismic sensing, which range from collecting sparse measurements with high-fidelity seismic stations to non-quantitative, post-earthquake surveys. The sparse nature of the high-fidelity stations and the inaccuracy of the surveys create the need for a high-density, semi-quantitative approach to seismic sensing. To fill this void, the UC Berkeley iShake project designed a mobile client-backend server architecture that uses sensor-equipped mobile devices to measure earthquake ground shaking. iShake provides the general public with a service to more easily contribute more quantitatively significant data to earthquake research by automating the data collection and reporting mechanisms via the iShake mobile application. The devices act as distributed sensors that enable measurements to be taken and transmitted with a cellular network connection. Shaking table testing was used to assess the quality of the measurements obtained from the iPhones and iPods on a benchmark of 150 ground motions. Once triggered by a shaking event, the devices transmit sensor data to a backend server for further processing. After a seismic event is verified by high-fidelity stations, filtering algorithms are used to detect falling phones, as well as device-specific responses to the event. A method was developed to determine the absolute orientation of a device to estimate the direction of first motion of a seismic event. A “virtual earthquake” pilot test was conducted on the UC Berkeley campus to verify the operation of the iShake system. By designing and fully implementing a system architecture, developing signal processing techniques unique to mobile sensing, and conducting shaking table tests to confirm the validity of the sensing platform, the iShake project serves as foundational work for further studies in seismic sensing on mobile devices.
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paper_title: Development of Mobile Radiation Monitoring System Utilizing Smartphone and Its Field Tests in Fukushima

paper_content:

We developed a series of inexpensive but accurate and mobile radiation detectors, which we named Pocket Geiger (POKEGA), to address the desire of ordinary people to own a radiation detector following the March 2011 Daiichi Nuclear Power Plant accidents in Fukushima, Japan. To reduce costs while maintaining accuracy and flexibility, we used a combination of a p-i-n photodiode detector connected to a smartphone via a microphone cable. The detector circuit design is optimized for simplicity and low cost, whereas the smartphone software application is tasked with handling the complex processing required. Furthermore, the device also used the GPS and networking capabilities of the smartphone for logging and data sharing. The 137Cs measurement range for a POKEGA-equipped smartphone is approximately from 0.05 to 10 mSv/h, which covers most radiation levels measured in Japan. Approximately 12000 POKEGA units were shipped in the six months following its release, and 2000 users have joined a Facebook community where they report measurement results and discuss hardware and software improvements. In parallel, we have addressed practical problems for POKEGA, such as vibration noise, energy consumption, and operating temperature, by conducting field tests in the Fukushima evacuation zone. The POKEGA series has been improved by solving such issues. This article reports on a new style of pragmatic sensor networking methodology, from the aspects of emergency response engineering, open-sourced development, and consumer-generated measurements.
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paper_title: Mobile sensor data collector using Android smartphone

paper_content:

In this paper, we present a system using an Android smartphone that collects, displays sensor data on the screen and streams to the central server simultaneously. Bluetooth and wireless Internet connections are used for data transmissions among the devices. Also, using Near Field Communication (NFC) technology, we have constructed a more efficient and convenient mechanism to achieve an automatic Bluetooth connection and application execution. This system is beneficial on body sensor networks (BSN) developed for medical healthcare applications. For demonstration purposes, an accelerometer, a temperature sensor and electrocardiography (ECG) signal data are used to perform the experiments. Raw sensor data are interpreted to either graphical or text notations to be presented on the smartphone and the central server. Furthermore, a Java-based central server application is used to demonstrate communication with the Android system for data storage and analysis.
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paper_title: Nericell: rich monitoring of road and traffic conditions using mobile smartphones

paper_content:

We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.
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paper_title: Sparse mobile crowdsensing: challenges and opportunities

paper_content:

Sensing cost and data quality are two primary concerns in mobile crowdsensing. In this article, we propose a new crowdsensing paradigm, sparse mobile crowdsensing, which leverages the spatial and temporal correlation among the data sensed in different sub-areas to significantly reduce the required number of sensing tasks allocated, thus lowering overall sensing cost (e.g., smartphone energy consumption and incentives) while ensuring data quality. Sparse mobile crowdsensing applications intelligently select only a small portion of the target area for sensing while inferring the data of the remaining unsensed area with high accuracy. We discuss the fundamental research challenges in sparse mobile crowdsensing, and design a general framework with potential solutions to the challenges. To verify the effectiveness of the proposed framework, a sparse mobile crowdsensing prototype for temperature and traffic monitoring is implemented and evaluated. With several future research directions identified in sparse mobile crowdsensing, we expect that more research interests will be stimulated in this novel crowdsensing paradigm.
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paper_title: Compressive sensing based data quality improvement for crowd-sensing applications

paper_content:

Crowd-sensing enables to collect a vast amount of data from the crowd by allowing a wide variety of sources to contribute data. However, the openness of crowd-sensing exposes the system to malicious and erroneous participations, inevitably resulting in poor data quality. This brings forth an important issue of false data detection and correction in crowd-sensing. Furthermore, data collected by participants normally include considerable missing values, which poses challenges for accurate false data detection. In this work, we propose Deco, a general framework to detect false values for crowd-sensing in the presence of missing data. By applying a tailored spatio-temporal compressive sensing technique, Deco is able to accurately detect the false data and estimate both false and missing values for data correction. Through comprehensive performance evaluations, we demonstrate the efficacy of Deco in achieving false data detection and correction for crowd-sensing applications with incomplete sensory data.
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paper_title: Mobile crowd sensing in space weather monitoring: the mahali project

paper_content:

Space weather refers to the conditions and evolution of Earth's near space environment including electron density variations in the ionosphere. This environment is influenced by both the Sun and terrestrial processes, and has an impact on communications, navigation, and terrestrial power systems. The recent discovery of clear signatures in the ionosphere related to tsunamis and earthquakes suggests that the ionosphere itself may serve as a valuable and versatile sensor, registering many types of Earth- and space-based phenomena. To realize this potential, ionospheric electron density must be monitored through a dense wide-area sensor mesh that is expensive to realize with traditional deployments and observation techniques. Crowdsourcing can help pursue this novel direction by providing new capabilities, including an increase in the number of sensors as well as expanding data transport capabilities through participating devices that act as relays. This article describes the Mahali project, which is currently at the beginning of exploring these promising techniques. Mahali uses GPS signals that penetrate the ionosphere for science rather than positioning. A large number of ground-based sensors will be able to feed data through mobile devices into a cloud-based processing environment, enabling a tomographic analysis of the global ionosphere at unprecedented resolution and coverage. This novel approach brings the exploitation of the ionosphere as a global earth system sensor technologically and economically within reach.
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paper_title: Spatiotemporal Scheduling for Crowd Augmented Urban Sensing

paper_content:

In urban environments, mobile crowdsensing can be used to augment in-situ sensing deployments (e.g. for environmental and community monitoring) in a flexible and cost-efficient manner. The additional participation provided by crowdsensing enables improved data collection coverage and enhances timeliness of data delivery. However, as the number of participating devices/users increases, efficient management is required to handle the increased operational cost of the infrastructure and associated cloud services - exploiting spatiotemporal redundancy in sensing can help cost-efficient utilization of resources. In this paper, we develop solutions to exploit the mobility of the crowd and manage the sensing capability of participating devices to effectively meet application/user demands for hybrid urban sensing applications. Specifically, we address the spatiotemporal scheduling problem to create high-resolution maps (e.g. for pollution sensing) by developing a common framework to capture spatiotemporal impact of multiple sensor types that generate heterogeneous data at different levels of granularity. We develop an online scheduling approach that leverages the knowledge of device location and sensing capability to selectively activate nodes and sensors. We build a multi-sensor platform that enables data collection, data exchange, and node management. Prototype deployments in three different campus/community testbeds were instrumented for measurements. Traces collected from the testbeds are used to drive extensive large scale simulations. Results show that our proposed solution achieves improved data coverage and utility under data constraints with lower costs (30% fewer active nodes) than naive approaches.
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paper_title: LifeMap: A Smartphone-Based Context Provider for Location-Based Services

paper_content:

LifeMap, a smartphone-based context provider operating in real time, fuses accelerometer, digital compass, Wi-Fi, and GPS to track and automatically identify points of interest with room-level accuracy.
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paper_title: A Cost-Effective Distributed Framework for Data Collection in Cloud-Based Mobile Crowd Sensing Architectures

paper_content:

Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets, and wearable devices. To be effective, mobile crowd sensing systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and sustainable framework for gathering information in cloud-based mobile crowd sensing systems with opportunistic reporting. The proposed framework minimizes cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. Analytical and simulation results provide performance evaluation for the proposed framework by providing a fine-grained analysis of the energy consumed. The simulations, performed in a real urban environment and with a large number of participants, aim at verifying the performance and scalability of the proposed approach on a large scale under different user arrival patterns.
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paper_title: Common Sense: participatory urban sensing using a network of handheld air quality monitors

paper_content:

Poor air quality is a global health issue, causing serious problems like asthma, cancer, and heart disease around the world. Earlier this decade, the World Health Organization estimated that three million people die each year from the effects of air pollution [6]. Unfortunately, while variations in air quality are significant, today's air quality monitors are very sparsely deployed. To address this visibility gap, the Common Sense project is developing participatory sensing systems that allow individuals to measure their personal exposure, groups to aggregate their members' exposure, and activists to mobilize grassroots community action.
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paper_title: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

paper_content:

We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.
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paper_title: HealthAware: Tackling obesity with health aware smart phone systems

paper_content:

Obesity prevention requires individuals to have healthy eating and physical activity awareness in their daily lives. New technological products have been explored to help monitoring individual health behaviors. Smart phones are ubiq­uitous hand-held tools with rich embedded sensors and limited computing power. A smart phone is a prospective candidate to integrate physical exercise analysis and food intake monitoring. This paper presents a novel health-aware smart phone system (HealthAware) which utilizes the embedded accelerometer to monitor daily physical activities and the built-in camera to analyze food items. The system presents the user's physical activity counts at real time to remind how much daily activity is needed to keep healthy. The user takes pictures of food items and the system provides health related information regarding to the food item intakes. The system is composed of an on-device database which holds the user specific data and food item information. HealthAware is a real time practical system to prevent obesity by enhancing individual daily healthy behavior awareness.
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paper_title: Andwellness: An open mobile system for activity and experience sampling

paper_content:

Advances in mobile phone technology have allowed phones to become a convenient platform for real-time assessment of a participants health and behavior. AndWellness, a personal data collection system, uses mobile phones to collect and analyze data from both active, triggered user experience samples and passive logging of onboard environmental sensors. The system includes an application that runs on Android based mobile phones, server software that manages deployments and acts as a central repository for data, and a dashboard front end for both participants and researchers to visualize incoming data in real-time. Our system has gone through testing by researchers in preparation for deployment with participants, and we describe an initial qualitative study plus several planned future studies to demonstrate how our system can be used to better understand a user's health related habits and observations.
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paper_title: Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome

paper_content:

This paper describes a new real-time urban monitoring system. The system uses the Localizing and Handling Network Event Systems (LocHNESs) platform developed by Telecom Italia for the real-time evaluation of urban dynamics based on the anonymous monitoring of mobile cellular networks. In addition, data are supplemented based on the instantaneous positioning of buses and taxis to provide information about urban mobility in real time, ranging from traffic conditions to the movements of pedestrians throughout the city. This system was exhibited at the Tenth International Architecture Exhibition of the Venice Biennale. It marks the unprecedented monitoring of a large urban area, which covered most of the city of Rome, in real time using a variety of sensing systems and will hopefully open the way to a new paradigm of understanding and optimizing urban dynamics.
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paper_title: On the Feasibility of Social Network-based Pollution Sensing in ITSs

paper_content:

Intense vehicular traffic is recognized as a global ::: societal problem, with a multifaceted influence on the quality of ::: life of a person. Intelligent Transportation Systems (ITS) can ::: play an important role in combating such problem, decreasing ::: pollution levels and, consequently, their negative effects. One of ::: the goals of ITSs, in fact, is that of controlling traffic flows, ::: measuring traffic states, providing vehicles with routes that ::: globally pursue low pollution conditions. How such systems ::: measure and enforce given traffic states has been at the center of ::: multiple research efforts in the past few years. Although many ::: different solutions have been proposed, very limited effort has ::: been devoted to exploring the potential of social network analysis ::: in such context. Social networks, in general, provide direct ::: feedback from people and, as such, potentially very valuable ::: information. A post that tells, for example, how a person feels ::: about pollution at a given time in a given location, could be put to ::: good use by an environment aware ITS aiming at minimizing ::: contaminant emissions in residential areas. This work verifies the ::: feasibility of using pollution related social network feeds into ITS ::: operations. In particular, it concentrates on understanding how ::: reliable such information is, producing an analysis that confronts ::: over 1,500,000 posts and pollution data obtained from on-thefield ::: sensors over a one-year span.
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paper_title: TaskMe: multi-task allocation in mobile crowd sensing

paper_content:

Task allocation or participant selection is a key issue in Mobile Crowd Sensing (MCS). While previous participant selection approaches mainly focus on selecting a proper subset of users for a single MCS task, multi-task-oriented participant selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes TaskMe, a participant selection framework for multi-task MCS environments. In particular, two typical multi-task allocation situations with bi-objective optimization goals are studied: (1) For FPMT (few participants, more tasks), each participant is required to complete multiple tasks and the optimization goal is to maximize the total number of accomplished tasks while minimizing the total movement distance. (2) For MPFT (more participants, few tasks), each participant is selected to perform one task based on pre-registered working areas in view of privacy, and the optimization objective is to minimize total incentive payments while minimizing the total traveling distance. Two optimal algorithms based on the Minimum Cost Maximum Flow theory are proposed for FPMT, and two algorithms based on the multi-objective optimization theory are proposed for MPFT. Experiments verify that the proposed algorithms outperform baselines based on a large-scale real-word dataset under different experiment settings (the number of tasks, various task distributions, etc.).
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paper_title: Dynamic Task Assignment in Crowdsensing with Location Awareness and Location Diversity

paper_content:

Crowdsensing paradigm facilitates a wide range of data collection, where great efforts have been made to address its fundamental issue of matching workers to their assigned tasks. In this paper, we reexamine this issue by considering the spatiotemporal worker mobility and task arrivals, which more fits the actual situation. Specifically, we study the location-aware and location diversity based dynamic crowdsensing system, where workers move over time and tasks arrive stochastically. We first exploit offline crowdsensing by proposing a combinatorial algorithm, for efficiently distributing tasks to workers. After that, we mainly study the online crowdsensing, and further consider an indispensable aspect of worker's fair allocation. Apart from the stochastic characteristics and discontinuous coverage, the nonlinear expectation is incurred as a new challenge concerning fairness issue. Based on Lyapunov optimization with perturbation parameters, we propose online control policy to overcome those challenges. Hereby we can maintain system stability and achieve a time average sensing utility arbitrarily close to the optimum. Performance evaluation on real data set validates the proposed algorithm, where 116% gain of fairness is achieved at the expense of 12% loss of sensing value on average.
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paper_title: Deadline-sensitive User Recruitment for mobile crowdsensing with probabilistic collaboration

paper_content:

Mobile crowdsensing is a new paradigm in which a group of mobile users exploit their smart devices to cooperatively perform a large-scale sensing job over urban environments. In this paper, we focus on the Deadline-sensitive User Recruitment (DUR) problem for probabilistically collaborative mobile crowdsensing. Unlike previous works, mobile users in this problem perform sensing tasks with probabilities, and multiple users might be recruited to cooperatively perform a common task, ensuring that the expected completion time is no larger than a deadline. Owing to such a probabilistic collaboration, DUR can be formalized as a non-trivial set cover problem with non-linear programming constraints and an optimization objective of real function. We first prove that the DUR problem is NP-hard. Then, we propose a greedy DUR algorithm, called gDUR, to solve this problem. Next, we prove that the gDUR algorithm can achieve a logarithmic approximation ratio. Furthermore, we extend the problem to a more complex case where sensing duration is taken into consideration, and we propose a sensing-duration-aware user recruitment algorithm, called dDUR. Finally, we validate the performance of the proposed algorithms through extensive simulations, based on a real mobile social network trace and a synthetic trace.
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paper_title: User recruitment for mobile crowdsensing over opportunistic networks

paper_content:

We look into the realization of mobile crowdsensing campaigns that draw on the opportunistic networking paradigm, as practised in delay-tolerant networks but also in the emerging device-to-device communication mode in cellular networks. In particular, we ask how mobile users can be optimally selected in order to generate the required space-time paths across the network for collecting data from a set of fixed locations. The users hold different roles in these paths, from collecting data with their sensing-enabled devices to relaying them across the network and uploading them to data collection points with Internet connectivity. We first consider scenarios with deterministic node mobility and formulate the selection of users as a minimum-cost set cover problem with a submodular objective function. We then generalize to more realistic settings with uncertainty about the user mobility. A methodology is devised for translating the statistics of individual user mobility to statistics of spacetime path formation and feeding them to the set cover problem formulation. We describe practical greedy heuristics for the resulting NP-hard problems and compute their approximation ratios. Our experimentation with real mobility datasets (a) illustrates the multiple tradeoffs between the campaign cost and duration, the bound on the hopcount of space-time paths, and the number of collection points; and (b) provides evidence that in realistic problem instances the heuristics perform much better than what their pessimistic worst-case bounds suggest.
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paper_title: Energy-Efficient Transmission With Data Sharing in Participatory Sensing Systems

paper_content:

In a participatory sensing system, data sensed from smartphone users are shared with the general public who requests data through submitting tasks. When multiple tasks request the data from a mobile user, the mobile user can make a transmission schedule to achieve the balance between the amount of data transmitted and energy consumption. Intuitively, reducing the amount of data transmitted by making use of data sharing between the tasks can save the energy consumption. However, due to the convexity of rate-power function for rate-adaptive transmitting devices, a schedule purely minimizing the amount of data transmitted may not always be the optimal one minimizing the energy consumption. Thus, there exists a tradeoff between the amount of data transmitted and energy consumption. This paper formulates the problem as a bi-objective optimization problem to simultaneously minimize the amount of data transmitted and the energy consumption. Two task models are studied, first-in-first-out (FIFO) task model and arbitrary deadline (AD) task model, respectively. We first provide optimal algorithms for the off-line case. We then study the online case where requests arrive dynamically without prior information. For FIFO tasks, we develop an online algorithm that is $O(\ln L)$ -competitive with respect to both the amount of data transmitted and energy consumption, where $L$ is the longest length of the time duration of the tasks. For AD tasks, we devise an online algorithm that is $O(\ln ^{2} L)$ -competitive with respect to both the amount of data transmitted and energy consumption. Our simulation results validate the efficiency of our online algorithms.
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paper_title: iCrowd: Near-Optimal Task Allocation for Piggyback Crowdsensing

paper_content:

This paper first defines a novel spatial-temporal coverage metric, $k$ -depth coverage, for mobile crowdsensing (MCS) problems. This metric considers both the fraction of subareas covered by sensor readings and the number of sensor readings collected in each covered subarea. Then iCrowd , a generic MCS task allocation framework operating with the energy-efficient Piggyback Crowdsensing task model, is proposed to optimize the MCS task allocation with different incentives and $k$ -depth coverage objectives/constraints. iCrowd first predicts the call and mobility of mobile users based on their historical records, then it selects a set of users in each sensing cycle for sensing task participation, so that the resulting solution achieves two dual optimal MCS data collection goals—i.e., Goal. 1 near-maximal $k$ -depth coverage without exceeding a given incentive budget or Goal. 2 near-minimal incentive payment while meeting a predefined $k$ -depth coverage goal. We evaluated iCrowd extensively using a large-scale real-world dataset for these two data collection goals. The results show that: for Goal.1, iCrowd significantly outperformed three baseline approaches by achieving 3-60 percent higher $k$ -depth coverage; for Goal.2, iCrowd required 10.0-73.5 percent less incentives compared to three baselines under the same $k$ -depth coverage constraint.
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paper_title: When data acquisition meets data analytics: A distributed active learning framework for optimal budgeted mobile crowdsensing

paper_content:

An important category of mobile crowdsensing applications involve collecting sensor measurements from mobile devices and querying mobile users for annotations to build machine learning models for inference and prediction. Trade-offs between inference performance and the costs of data acquisition (both unlabeled and labeled) are not yet well understood. In this paper, we develop, ALSense, a distributed active learning framework for mobile crowdsensing. The goal is to minimize prediction errors for classification-based mobile crowdsensing tasks subject to upload and query cost constraints. Novel stream-based active learning strategies are developed to orchestrate queries of annotation data and the upload of unlabeled data from mobile devices. We evaluate the effectiveness of ALSense through two applications that can benefit from mobile crowdsensing, namely, WiFi fingerprint-based indoor localization and IMU-based human activity recognition. Extensive experiments demonstrate that ALSense can indeed achieve higher classification accuracy given fixed data acquisition budgets for both applications.
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paper_title: Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing

paper_content:

Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.
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paper_title: Near-Optimal Allocation Algorithms for Location-Dependent Tasks in Crowdsensing

paper_content:

Crowdsensing offers an efficient way to meet the demand in large-scale sensing applications. In crowdsensing, optimal task allocation is challenging since sensing tasks with different requirements of quality of sensing are typically associated with specific locations, and mobile users have time constraints. We show that the allocation problem is NP-hard. We then focus on approximation algorithms and devise an efficient local-ratio-based algorithm (LRBA). Our analysis shows that the approximation ratio of the aggregate rewards obtained by optimal allocation to those by LRBA is 5. This reveals that LRBA is efficient, since a lower (but not tight) bound on the approximation ratio is 4. We extend the results to the general scenario where mobile users are heterogeneous. A distributed version of LRBA, namely DLRBA, is designed, which can be iteratively executed at each mobile user without the need for the platform to collect all the information of mobile users. We prove that both centralized and distributed versions can output the same solution. Extensive simulation results are provided to demonstrate the advantages of our proposed algorithms.
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paper_title: An Efficient Prediction-Based User Recruitment for Mobile Crowdsensing

paper_content:

Mobile crowdsensing is a new paradigm in which a group of mobile users exploit their smart devices to cooperatively perform a large-scale sensing job. One of the users’ main concerns is the cost of data uploading, which affects their willingness to participate in a crowdsensing task. In this paper, we propose an efficient Prediction-based User Recruitment for mobile crowdsEnsing (PURE), which separates the users into two groups corresponding to different price plans: Pay as you go (PAYG) and Pay monthly (PAYM). By regarding the PAYM users as destinations, the minimizing cost problem goes to recruiting the users that have the largest contact probability with a destination. We first propose a semi-Markov model to determine the probability distribution of user arrival time at points of interest (PoIs) and then get the inter-user contact probability. Next, an efficient prediction-based user-recruitment strategy for mobile crowdsensing is proposed to minimize the data uploading cost. We then propose PURE-DF by extending PURE to a case in which we address the tradeoff between the delivery ratio of sensing data and the recruiter number according to Delegation Forwarding. We conduct extensive simulations based on three widely-used real-world traces: roma/taxi , epfl , and geolife . The results show that, compared with other recruitment strategies, PURE achieves a lower recruitment payment and PURE-DF achieves the highest delivery efficiency.
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paper_title: Truthful Scheduling Mechanisms for Powering Mobile Crowdsensing

paper_content:

Mobile crowdsensing leverages mobile devices (e.g., smart phones) and human mobility for pervasive information exploration and collection; it has been deemed as a promising paradigm that will revolutionize various research and application domains. Unfortunately, the practicality of mobile crowdsensing can be crippled due to the lack of incentive mechanisms that stimulate human participation. In this paper, we study incentive mechanisms for a novel Mobile Crowdsensing Scheduling (MCS) problem, where a mobile crowdsensing application owner announces a set of sensing tasks, then human users (carrying mobile devices) compete for the tasks based on their respective sensing costs and available time periods, and finally the owner schedules as well as pays the users to maximize its own sensing revenue under a certain budget. We prove that the MCS problem is NP-hard and propose polynomial-time approximation mechanisms for it. We also show that our approximation mechanisms (including both offline and online versions) achieve desirable game-theoretic properties, namely truthfulness and individual rationality, as well as $\mathcal {O}(1)$ performance ratios. Finally, we conduct extensive simulations to demonstrate the correctness and effectiveness of our approach.
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paper_title: A Budget Feasible Incentive Mechanism for Weighted Coverage Maximization in Mobile Crowdsensing

paper_content:

Mobile crowdsensing is a novel paradigm to collect sensing data and extract useful information about regions of interest. It widely employs incentive mechanisms to recruit a number of mobile users to fulfill coverage requirement in the interested regions. In practice, sensing service providers face a pressing optimization problem: How to maximize the valuation of the covered interested regions under a limited budget? However, the relation between two important factors, i.e., Coverage Maximization and Budget Feasibility , has not been fully studied in existing incentive mechanisms for mobile crowdsensing. Furthermore, the existing approaches on coverage maximization in sensor networks can work, when mobile users are rational and selfish. In this paper, we present the first in-depth study on the coverage problem for incentive-compatible mobile crowdsensing, and propose BEACON, which is a B udget f EA sible and strategy-proof incentive mechanism for weighted CO verage maximizatio N in mobile crowdsensing. BEACON employs a novel monotonic and computationally tractable approximation algorithm for sensing task allocation, and adopts a newly designed proportional share rule based compensation determination scheme to guarantee strategy-proofness and budget feasibility. Our theoretical analysis shows that BEACON can achieve strategy-proofness, budget feasibility, and a constant-factor approximation. We deploy a noise map crowdsensing system to capture the noise level in a selected campus, and evaluate the system performance of BEACON on the collected sensory data. Our evaluation results demonstrate the efficacy of BEACON.
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paper_title: Load balance vs utility maximization in mobile crowd sensing: A distributed approach

paper_content:

This paper focuses on workload allocation among mobile nodes in a mobile crowd sensing system. We take both two important objectives into account, including load balance and sensing data utility maximization. However, workload allocation achieving both objectives is particularly challenging. First, there is an intrinsic tradeoff between load balance and utility maximization. The system should strike a good balance between the two important objectives. Second, the number of mobile users can be large. A simple exhaustive search of workload allocation results can be prohibitively expensive. In this paper, we model workload allocation as a Nash bargaining game. We propose a distributed algorithm to solve the Nash bargaining game and determine the workload to each individual smartphone. It effectively decomposes the complex optimization problem into subproblems, and obtains the workload allocation solution by an iterative procedure imitating the bargaining process. This distributed algorithm can achieve a fair tradeoff between workload balance and data utility maximization, which is provably Pareto-efficient. We have conducted extensive simulations, and the results demonstrate that our algorithm achieves Pareto optimality and fairness between the two important objectives.
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paper_title: Mobile crowd sensing task optimal allocation: a mobility pattern matching perspective

paper_content:

With the proliferation of sensor-equipped portable mobile devices, Mobile CrowdSensing (MCS) using smart devices provides unprecedented opportunities for collecting enormous surrounding data. In MCS applications, a crucial issue is how to recruit appropriate participants from a pool of available users to accomplish released tasks, satisfying both resource efficiency and sensing quality. In order to meet these two optimization goals simultaneously, in this paper, we present a novel MCS task allocation framework by aligning existing task sequence with users’ moving regularity as much as possible. Based on the process of mobility repetitive pattern discovery, the original task allocation problem is converted into a pattern matching issue, and the involved optimization goals are transformed into pattern matching length and support degree indicators. To determine a trade-off between these two competitive metrics, we propose greedy-based optimal assignment scheme search approaches, namely MLP, MDP, IU1 and IU2 algorithm, with respect to matching length-preferred, support degree-preferred and integrated utility, respectively. Comprehensive experiments on realworld open data set and synthetic data set clearly validate the effectiveness of our proposed framework on MCS task optimal allocation.
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paper_title: Towards energy-efficient task scheduling on smartphones in mobile crowd sensing systems

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In a mobile crowd sensing system, a smartphone undertakes many different sensing tasks that demand data from various sensors. In this paper, we consider the problem of scheduling different sensing tasks assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring Quality of SenSing (QoSS). First, we consider a simple case in which each sensing task only requests data from a single sensor. We formally define the corresponding problem as the Minimum Energy Single-sensor task Scheduling (MESS) problem and present a polynomial-time optimal algorithm to solve it. Furthermore, we address a more general case in which some sensing tasks request multiple sensors to report their measurements simultaneously. We present an Integer Linear Programming (ILP) formulation as well as two effective polynomial-time heuristic algorithms, for the corresponding Minimum Energy Multi-sensor task Scheduling (MEMS) problem. Extensive simulation results show that the proposed algorithms achieve significant energy savings, compared to a widely-used baseline approach; moreover, the proposed heuristic algorithms produce close-to-optimal solutions.
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paper_title: Heterogeneous Multi-Task Assignment in Mobile Crowdsensing Using Spatiotemporal Correlation

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Mobile crowdsensing (MCS) is a new paradigm to collect sensing data and infer useful knowledge over a vast area for numerous monitoring applications. In urban environments, as more and more applications need to utilize multi-source sensing information, it is almost indispensable to develop a generic mechanism supporting multiple concurrent MCS task assignment. However, most existing multi-task assignment methods focus on homogeneous tasks. Due to the diverse spatiotemporal task requirements and sensing contexts, MCS tasks often differ from each other in many aspects (e.g., spatial coverage, temporal interval). To this end, in the paper, we present and formalize an important Heterogeneous Multi-Task Assignment (HMTA) problem in mobile crowdsensing systems, and try to maximize data quality and minimize total incentive budget. By leveraging the implicit spatiotemporal correlations among heterogeneous tasks, we propose a two-stage HMTA problem-solving approach to effectively handle multiple concurrent tasks in a shared resource pool. Finally, in order to improve the assignment search efficiency, a decomposition-and-combination framework is devised to accommodate large-scale problem scenario. We evaluate our approach extensively using two large-scale real-world data sets. The experimental results validate the effectiveness and efficiency of our proposed approach.
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paper_title: Generic and Energy-Efficient Context-Aware Mobile Sensing

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Elaborating on the concept of context awareness, this book presents up-to-date research and novel framework designs for context-aware mobile sensing. Generic and Energy-Efficient Context-Aware Mobile Sensing proposes novel context-inferring algorithms and generic framework designs that can help readers enhance existing tradeoffs in mobile sensing, especially between accuracy and power consumption.The book presents solutions that emphasize must-have system characteristics such as energy efficiency, accuracy, robustness, adaptability, time-invariance, and optimal sensor sensing. Numerous application examples guide readers from fundamental concepts to the implementation of context-aware-related algorithms and frameworks.Covering theory and practical strategies for context awareness in mobile sensing, the book will help readers develop the modeling and analysis skills required to build futuristic context-aware framework designs for resource-constrained platforms. Includes best practices for designing and implementing practical context-aware frameworks in ubiquitous/mobile sensing Proposes a lightweight online classification method to detect user-centric postural actions Examines mobile device-based battery modeling under the scope of battery nonlinearities with respect to variant loads Unveils a novel discrete time inhomogeneous hidden semi-Markov model (DT-IHS-MM)-based generic framework to achieve a better realization of HAR-based mobile context awareness Supplying theory and equation derivations for all the concepts discussed, the book includes design tips for the implementation of smartphone programming as well as pointers on how to make the best use of MATLAB for the presentation of performance analysis. Coverage includes lightweight, online, and unsupervised pattern recognition methods; adaptive, time-variant, and optimal sensory sampling strategies; and energy-efficient, robust, and inhomogeneous context-aware framework designs. Researchers will learn the latest modeling and analysis research on mobile sensing. Students will gain access to accessible reference material on mobile sensing theory and practice. Engineers will gain authoritative insights into cutting-edge system designs.
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paper_title: Context-Awareness for Mobile Sensing: A Survey and Future Directions

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The evolution of smartphones together with increasing computational power has empowered developers to create innovative context-aware applications for recognizing user-related social and cognitive activities in any situation and at any location. The existence and awareness of the context provide the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze, and share local sensory knowledge in the purpose for a large-scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects and also assist individuals. However, many open challenges remain, which are mostly arisen because the middleware services provided in mobile devices have limited resources in terms of power, memory, and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved and, at the same time, better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991–2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlightens them by proposing possible solutions.
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paper_title: Posted Pricing for Chance Constrained Robust Crowdsensing

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Crowdsensing has been well recognized as a promising approach to enable large scale urban data collection. In a typical crowdsensing system, the task owner usually needs to provide incentives to the users (say participants) to encourage their participation. Among existing incentive mechanisms, posted pricing has been widely adopted because it is easy to implement while ensuring truthfulness and fairness. One critical challenge to the task owner is to set the right posted price to recruit a crowd with small total payment and reasonable sensing quality, i.e., posted pricing problem for robust crowdsensing. However, this fundamental problem remains largely open so far. In this paper, we model the robustness requirement over sensing data quality as chance constraints in an elegant manner, and study a series of chance constrained posted pricing problems in crowdsensing systems. Although some chance-constrained optimization techniques have been applied in the literature, they cannot provide any performance guarantees for their solutions. In this work, we propose a binary search based algorithm, and show that using this algorithm allows us to establish theoretical guarantees on its performance. Extensive numerical simulations demonstrate the effectiveness of our proposed algorithm.
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paper_title: ActiveCrowd: A Framework for Optimized Multitask Allocation in Mobile Crowdsensing Systems

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Worker selection is a key issue in mobile crowd sensing (MCS). While the previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, a multitask-oriented worker selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes ActiveCrowd, a worker selection framework for multitask MCS environments. We study the problem of multitask worker selection under two situations: worker selection based on workers’ intentional movement for time-sensitive tasks and unintentional movement for delay-tolerant tasks. For time-sensitive tasks, workers are required to move to the task venue intentionally and the goal is to minimize the total distance moved. For delay-tolerant tasks, we select workers whose route is predicted to pass by the task venues and the goal is to minimize the total number of workers. Two greedy-enhanced genetic algorithms are proposed to solve them. Experiments verify that the proposed algorithms outperform baseline methods under different experiment settings (scale of task sets, available workers, varied task distributions, etc.).
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paper_title: Taming the uncertainty: budget limited robust crowdsensing through online learning

paper_content:

Mobile crowdsensing has been intensively explored recently due to its flexible and pervasive sensing ability. Although many crowdsensing platforms have been built for various applications, the general issue of how to manage such systems intelligently remains largely open. While recent investigations mostly focus on incentivizing crowdsensing, the robustness of crowdsensing toward uncontrollable sensing quality, another important issue, has been widely neglected. Due to the non-professional personnel and devices, the quality of crowdsensing data cannot be fully guaranteed, hence the revenue gained from mobile crowdsensing is generally uncertain. Moreover, the need for compensating the sensing costs under a limited budget has exacerbated the situation: one does not enjoy an infinite horizon to learn the sensing ability of the crowd and hence to make decisions based on sufficient statistics. In this paper, we present a novel framework, Budget LImited robuSt crowdSensing (BLISS), to handle this problem through an online learning approach. Our approach aims to minimize the difference on average sense (a.k.a. regret) between the achieved total sensing revenue and the (unknown) optimal one, and we show that our BLISS sensing policies achieve logarithmic regret bounds and Hannan-consistency. Finally, we use extensive simulations to demonstrate the effectiveness of BLISS.
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paper_title: Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints

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Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task, e.g., reporting the precipitation level at their area and time. In this setting, there is often a budget constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint, despite the dynamic arrivals of workers and tasks as well as their co-location relationship. We study two problem variants in this paper: budget is constrained for every timestamp, i.e. fixed, and budget is constrained for the entire campaign, i.e. dynamic. For each variant, we study the complexity of its offline version and then propose several heuristics for the online version which exploit the spatial and temporal knowledge acquired over time. Extensive experiments with real-world and synthetic datasets show the effectiveness and efficiency of our proposed solutions.
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paper_title: Crowdsourcing Sensing Workloads of Heterogeneous Tasks: A Distributed Fairness-Aware Approach

paper_content:

Crowd sourced sensing over smartphones presents a new paradigm for collecting sensing data over a vast area for real-time monitoring applications. A monitoring application may require different types of sensing data, while under a budget constraint. This paper explores the crucial problem of maximizing the aggregate data utility of heterogeneous sensing tasks while maintaining utility-centric fairness across different tasks under a budget constraint. In particular, we take the redundancy of sensing data into account. This problem is highly challenging given its unique characteristics including the intrinsic trade off between aggregate data utility and fairness, and the large number of smartphones. We propose a fairness-aware distributed approach to solving this problem. To overcome the intractability of the problem, we decompose it to two sub problems of recruiting smartphones under a budget constraint and allocating workloads of sensing tasks. For the first sub problem, we propose an efficient greedy algorithm which has a constant approximation ratio of two. For the second problem, we apply dual based decomposition based on which we design a distributed algorithm for determining the workloads of different tasks on each recruited smartphone. We have implemented our distributed algorithm on a windows-based server and Android-based smartphones. With extensive simulations we demonstrate that our approach achieves high aggregate data utility while maintaining good utility-centric fairness across sensing tasks.
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paper_title: Context-Aware Crowd-Sensing in Opportunistic Mobile Social Networks

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In this paper, we study the physical crowd-sensing problem and draw the connection to the vertex cover problem in graph theory. Since finding the optimal solution for minimum vertex cover problem is NP-complete and the well-known approximation algorithms do not perform well with under crowd-sensing scenario, we propose the notions of node observability and coverage utility score and design a new context-aware approximation algorithm to find vertex cover that is tailored for crowd-sensing task. In addition, we design human-centric bootstrapping strategies to make initial assignment of sensing devices in the physical crowd based on social information about the users (e.g., Interests, friendship). Our experiments on real-world data traces show that the proposed approach significantly outperforms the baseline approximation algorithms in terms of sensing coverage.
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paper_title: High quality participant recruitment in vehicle-based crowdsourcing using predictable mobility

paper_content:

The potential of crowdsourcing for complex problem solving has been revealed by smartphones. Nowadays, vehicles have also been increasingly adopted as participants in crowd-sourcing applications. Different from smartphones, vehicles have the distinct advantage of predictable mobility, which brings new insight into improving the crowdsourcing quality. Unfortunately, utilizing the predictable mobility in participant recruitment poses a new challenge of considering not only current location but also the future trajectories of participants. Therefore, existing participant recruitment algorithms that only use the current location may not perform well. In this paper, based on the predicted trajectory, we present a new participant recruitment strategy for vehicle-based crowdsourcing. This strategy guarantees that the system can perform well using the currently recruited participants for a period of time in the future. The participant recruitment problem is proven to be NP-complete, and we propose two algorithms, a greedy approximation and a genetic algorithm, to find the solution for different application scenarios. The performance of our algorithms is demonstrated with traffic trace dataset. The results show that our algorithms outperform some existing approaches in terms of the crowdsourcing quality.
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paper_title: TRAC: Truthful auction for location-aware collaborative sensing in mobile crowdsourcing

paper_content:

In this paper, we tackle the problem of stimulating smartphone users to join mobile crowdsourcing applications with smartphones. Different from existing work of mechanism design, we uniquely take into consideration thecrucial dimension of location informationwhen assigning sensing tasks to smartphones. However, the location awareness largely increases the theoretical and computational complexity. In this paper, we introduce a reverse auction frameworkto model the interactions between the platform and the smartphones. We rigorously prove that optimally determining the winning bids isNP hard. In this paper we design a mechanism called TRACwhich consists of two main components. The first component is a near-optimal approximate algorithm for determining the winning bids with polynomial-time computation complexity, which approximates the optimal solution within a factor of 1+l n(n), wheren is the maximum number of sensing tasks that a smartphone can accommodate. The second component is a critical payment scheme which, despite the approximation of determining winning bids, guarantees that submitted bids of smartphones reflect their real costs of performing sensing tasks. Through both rigid theoretical analysis and extensive simulations, we demonstrate that the proposed mechanism achieves truthfulness, individual rationality and high computation efficiency.
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paper_title: Incentive Mechanisms for Crowdsensing: Crowdsourcing With Smartphones

paper_content:

Smartphones are programmable and equipped with a set of cheap but powerful embedded sensors, such as accelerometer, digital compass, gyroscope, GPS, microphone, and camera. These sensors can collectively monitor a diverse range of human activities and the surrounding environment. Crowdsensing is a new paradigm which takes advantage of the pervasive smartphones to sense, collect, and analyze data beyond the scale of what was previously possible. With the crowdsensing system, a crowdsourcer can recruit smartphone users to provide sensing service. Existing crowdsensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for crowdsensing. We consider two system models: the crowdsourcer-centric model where the crowdsourcer provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the crowdsourcer-centric model, we design an incentive mechanism using a Stackelberg game, where the crowdsourcer is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the crowdsourcer is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.
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paper_title: Dynamic Participant Recruitment of Mobile Crowd Sensing for Heterogeneous Sensing Tasks

paper_content:

With the rapid increasing of smart mobile devices and the advances of sensing technologies, mobile crowd sensing (MCS) becomes a new popular sensing paradigm, which enables a variety of large-scale sensing applications. One of the key challenges of large-scale mobile crowd sensing systems is how to effectively select appropriate participants from a huge user pool to perform various sensing tasks while satisfying certain constraints. This becomes more complex when the sensing tasks are dynamic (coming in real time) and heterogeneous (having different temporal and spacial requirements). In this paper, we consider such a dynamic participant recruitment problem with heterogeneous sensing tasks which aims to minimize the sensing cost while maintaining certain level of probabilistic coverage. Both offline and online algorithms are proposed to solve the challenging problem. Extensive simulations over a real-life mobile dataset confirm the efficiency of the proposed algorithms.
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paper_title: Physical Activity Recognition From Smartphone Accelerometer Data for User Context Awareness Sensing

paper_content:

Physical activity recognition of everyday activities such as sitting, standing, laying, walking, and jogging was performed, through the use of smartphone accelerometer data. Activity classification was done on a remote server through the use of machine learning algorithms, data was received from the smartphone wirelessly. The smartphone was placed in the subject’s trouser pocket while data was gathered. A large sample set was used to train the classifiers and then a test set was used to verify the algorithm accuracies. Ten different classifier algorithm configurations were evaluated to determine which performed best overall, as well as, which algorithms performed best for specific activity classes. Based on the results obtained, very accurate predictions could be made for offline activity recognition. The kNN and kStar algorithms both obtained an overall accuracy of 99.01%.
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paper_title: CrowdRecruiter: selecting participants for piggyback crowdsensing under probabilistic coverage constraint

paper_content:

This paper proposes a novel participant selection framework, named CrowdRecruiter, for mobile crowdsensing. CrowdRecruiter operates on top of energy-efficient Piggyback Crowdsensing (PCS) task model and minimizes incentive payments by selecting a small number of participants while still satisfying probabilistic coverage constraint. In order to achieve the objective when piggybacking crowdsensing tasks with phone calls, CrowdRecruiter first predicts the call and coverage probability of each mobile user based on historical records. It then efficiently computes the joint coverage probability of multiple users as a combined set and selects the near-minimal set of participants, which meets coverage ratio requirement in each sensing cycle of the PCS task. We evaluated CrowdRecruiter extensively using a large-scale real-world dataset and the results show that the proposed solution significantly outperforms three baseline algorithms by selecting 10.0% -- 73.5% fewer participants on average under the same probabilistic coverage constraint.
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paper_title: Distinguishing uncertain objects with multiple features for crowdsensing

paper_content:

The development of the smartphones with various sensors, and powerful capabilities (computing, storage, and communication), motivates a popular computing and sensing paradigm, crowdsensing. In general, in crowdsensing, the smart-phones sense and collect the sensory data from a large number of smartphone users, for distinguishing the uncertain objects. However, some existing solutions for crowdsensing usually prefer to utilize only one or few features to distinguish the uncertain objects. In this paper, due to the limitation of less features, we propose to utilize multiple features to distinguish the uncertain objects for crowdsensing. For distinguishing uncertain objects with multiple features, we propose to utilize KL divergence based clustering. Moreover, we introduce two other mutated forms, the symmetry KL divergence and Jensen-Shannon KL divergence, to improve our algorithm. We evaluate our proposed schemes with real data of multiple features, which are collected by the smartphones with the sensors.
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paper_title: VCAMS: Viterbi-Based Context Aware Mobile Sensing to Trade-Off Energy and Delay

paper_content:

Monitoring context depends on continuous collection of raw data from sensors which are either embedded in smart mobile devices or worn by the user. However, continuous sensing constitutes a major source of energy consumption; on the other hand, lowering the sensing rate may lead to missing the detection of critical contextual events. In this paper, we propose VCAMS: a Viterbi-based Context Aware Mobile Sensing mechanism that adaptively finds an optimized sensing schedule to decide when to trigger the sensors for data collection while trading off the sensing energy and the delay to detect a state change. The sensing schedule is adaptive from two aspects: 1) the decision rules are learned from the user’s past behavior, and 2) these rules are updated over real time whenever there is a significant change in the user’s behavior. VCAMS is validated using multiple experiments, which include evaluation of model success when considering binary and multi-user states. We also implemented VCAMS on an Android-based device to estimate its computational costs under realistic operational conditions. Test results show that our proposed strategy provides better trade-off than previous state-of-the-art methods under comparable conditions. Furthermore, the method provides 78 percent energy saving when compared to continuous sensing.
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paper_title: First learn then earn: optimizing mobile crowdsensing campaigns through data-driven user profiling

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We study the optimal design of mobile crowdsensing campaigns in terms of the aggregate quality of contributions attracted for a set of tasks. The interaction of the campaign with users is realized through a mobile app interface that recommends tasks to users and offers them incentives. The main contribution is a novel perspective on the payment distribution problem faced by the crowdsensing campaign organizer in light of originally unknown individual user preferences. Contrary to common practice, we acknowledge that users exhibit high diversity in decision making because they assess differently attributes related to a task such as their proximity to the place of interest (PoI), the payment made for contributing data, or the task context/theme. We draw on logistic-regression techniques from machine learning to learn users' individual preferences from past data rather than hypothesizing about them. We then formulate non-linear (sigmoid) optimization problems to determine the tasks and incentives (payments) that should be optimally offered to each user. Our mechanism is validated against synthetic but also real data about the way users choose tasks, collected through an online questionnaire. It achieves very good approximations of the optimal solutions and substantially outperforms alternative preference-agnostic policies that do not exercise behavioral user profiling to target the provision of incentives.
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paper_title: Task Allocation in Mobile Crowd Sensing: State-of-the-Art and Future Opportunities

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Mobile crowd sensing (MCS) is the special case of crowdsourcing, which leverages the smartphones with various embedded sensors and user’s mobility to sense diverse phenomenon in a city. Task allocation is a fundamental research issue in MCS, which is crucial for the efficiency and effectiveness of MCS applications. In this paper, we specifically focus on the task allocation in MCS systems. We first present the unique features of MCS allocation compared to generic crowdsourcing, and then provide a comprehensive review for diversifying problem formulation and allocation algorithms together with future research opportunities.
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paper_title: Context-Aware Mobile Sensors for Sensing Discrete Events in Smart Environment

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Over the last few decades, several advancements in the field of smart environment gained importance, so the experts can analyze ideas for smart building based on embedded systems to minimize the expense and energy conservation. Therefore, propelling the concept of smart home toward smart building, several challenges of power, communication, and sensors’ connectivity can be seen. Such challenges distort the interconnectivity between different technologies, such as Bluetooth and ZigBee, making it possible to provide the continuous connectivity among different objects such as sensors, actuators, home appliances, and cell phones. Therefore, this paper presents the concept of smart building based on embedded systems that enhance low power mobile sensors for sensing discrete events in embedded systems. The proposed scheme comprises system architecture that welcomes all the mobile sensors to communicate with each other using a single platform service. The proposed system enhances the concept of smart building in three stages (i.e., visualization, data analysis, and application). For low power mobile sensors, we propose a communication model, which provides a common medium for communication. Finally, the results show that the proposed system architecture efficiently processes, analyzes, and integrates different datasets efficiently and triggers actions to provide safety measurements for the elderly, patients, and others.
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paper_title: Crowdlet: Optimal worker recruitment for self-organized mobile crowdsourcing

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In this paper, we advocate Crowdlet, a novel self-organized mobile crowdsourcing paradigm, in which a mobile task requester can proactively exploit a massive crowd of encountered mobile workers at real-time for quick and high-quality results. We present a comprehensive system model of Crowdlet that defines task, worker arrival and worker ability models. Further, we introduce a service quality concept to indicate the expected service gain that a requester can enjoy when he recruits an encountered worker, by jointly taking into account worker ability, real-timeness and task reward. Based on the models, we formulate an online worker recruitment problem to maximize the expected sum of service quality. We derive an optimal worker recruitment policy through the dynamic programming principle, and show that it exhibits a nice threshold based structure. We conduct extensive performance evaluation based on real traces, and numerical results demonstrate that our policy can achieve superior performance and improve more than 30% performance gain over classic policies. Besides, our Android prototype shows that Crowdlet is cost-efficient, requiring less than 7 seconds and 6 Joule in terms of time and energy cost for policy computation in most cases.
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paper_title: Cost-efficient Multi-tasking in Coverage-aware Mobile Crowd Sensing

paper_content:

In Mobile Crowd Sensing (MCS), task assignment and user selection to minimize the total incentive cost is a crucial issue that has received a lot of attention recently. Previous works studied incentive techniques for single-task assignment schemes, but multi-task assignment schemes are a more practical consideration as MCS applications are becoming increasingly popular. In this paper, we aim at minimizing the total incentive cost for all task publishers while considering related variables such as coverage, workload and sensor availability. Specifically, we motivate and propose a cost-efficient incentive mechanism that encourages multi-tasking versus a single-task assignment scenario. We present a novel metric called Combined Satisfaction Factor (CSF) that measures the total amount of satisfaction for both task publishers and task workers. We conduct simulations experiments to test our proposed scheme and show that our scheme improves the cost-efficiency for task creators by 13% and significantly increases the satisfaction levels in the system.
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paper_title: Quality of Sensing Aware Budget Feasible Mechanism for Mobile Crowdsensing

paper_content:

In a mobile crowdsensing system, the platform utilizes ubiquitous smartphones to perform sensing tasks. For a successful mobile crowdsensing application, the consideration of the heterogeneity of quality of sensing from different users as well as a proper incentive mechanism to motivate users to contribute to the system are essential. In this paper, we introduce the quality of sensing into incentive mechanism design. Under a budget constraint, the platform aims to maximize the valuation of the performed tasks, which depends on the quality of sensing of the users. We propose ABSee, an auction-based budget feasible mechanism, which consists of a winner selection rule and a payment determination rule. ABSee is designed by adopting a greedy approach. We obtain the approximation ratio of ABSee, which significantly improves the approximation ratio of the existing budget feasible mechanisms in many cases. We further show that the approximation ratio approaches $\frac {2e}{e-1}$ when a large number of smartphone users participate in the system. ABSee also satisfies the properties of computational efficiency, truthfulness, individual rationality, and budget feasibility. Extensive simulation results show that ABSee provides a higher valuation to the platform when compared with existing budget feasible mechanisms in the literature.
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paper_title: Distance and time based node selection for probabilistic coverage in People-Centric Sensing

paper_content:

Aiming to achieve sensing coverage for a given Area of Interest (AoI) in a People-Centric Sensing (PCS) manner, we propose a concept of (α, T)-coverage of the target field where each point in the field is sensed by at least one node with probability of at least α during the time period T. Our goal is to achieve (α, T)-coverage by a minimal set of mobile sensor nodes for a given AoI, coverage ratio α, and time period T. We model pedestrians as mobile sensor nodes moving according to a discrete Markov chain. Based on this model, we propose two algorithms: the inter-location and inter-meeting-time algorithms, to meet a coverage ratio α in time period T. These algorithms estimate the expected coverage of the specified AoI for a set of selected nodes. The inter-location algorithm selects a minimal number of mobile sensor nodes from nodes inside the AoI taking into account the distance between them. The inter-meeting-time selects nodes taking into account the expected meeting time between the nodes. We conducted a simulation study to evaluate the performance of the proposed algorithms for various parameter setting including a realistic scenario on a specific city map. The simulation results show that our algorithms achieve (α, T)-coverage with good accuracy for various values of α, T, and AoI size.
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paper_title: Queuing Algorithm for Effective Target Coverage in Mobile Crowd Sensing

paper_content:

In recent years, various researches have been conducted in order to find ways to cover a target or groups of targets with priority-based target coverage and sensor deployment mechanisms taking the front seats. However, with these researches, effective target coverage has been a recurrent issue due to various factors like conflict between sensors and excessive waiting time for targets to be covered. In this paper, we proposed an algorithm based on queuing theory in tandem with mobile crowd sensing to tackle these issues. To do this, first, we develop some models which are based on the birth-and-death mechanism (one of the tools in queuing theory) to determine how long a target has to wait, the mean busy period of sensors and mean idle period of sensors. While developing these models, we consider cases where there exist a single sensor and $n$ -sensors in the system. Based on these models, we develop the required algorithm. The simulation result shows that as the number of sensors increases relative to the number of targets, an average time before a target gets discovered is 0.2 s and sensor utilization decreasing toward zero as the number of sensors increases.
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paper_title: Minimum-Cost Recruitment of Mobile Crowdsensing in Cellular Networks

paper_content:

Mobile crowdsensing (MCS) is a promising paradigm that utilizes the mobility of people and the sensing capabilities of their mobile devices to accomplish a variety of sensing tasks. In this paper, we adopt the Signaling System No.7 (SS7) as the MCS platform since SS7 can well capture trajectories and mobility patterns of the mobile users. We collect a real-world SS7 data of 1.18 million mobile users at 3512 cell towers/sites in Xiamen, China. We first analyze this dataset and reveal important characteristics of user mobility. Then, we address a Mobile User Recruitment (MUR) problem which is crucial to all MCS systems. Given SS7 data of mobile users, a set of target cells to be sensed/covered, and recruitment cost functions of the mobile users, the MUR problem is to recruit a set of mobile users such that all the target cells are covered and the total recruitment cost is minimized. Our MUR problem is general and includes the existing problems as its special cases. We prove NP-hardness of the problem. We propose an approximation algorithm to this problem and derive the approximation ratio. Extensive experiments are conducted on the real-world SS7 dataset and results show that the proposed solution outperforms two baseline algorithms by saving 22.6% and 62.9% recruitment costs, respectively, on average.
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paper_title: Adaptive Activity and Context Recognition Using Multimodal Sensors in Smart Devices

paper_content:

The continuous development of new technologies has led to the creation of a wide range of personal devices embedded with an ever increasing number of miniature sensors. With accelerometers and technologies such as Bluetooth and Wi-Fi, today’s smartphones have the potential to monitor and record a complete history of their owners’ movements as well as the context in which they occur. In this article, we focus on four complementary aspects related to the understanding of human behaviour. First, the use of smartwatches in combination with smartphones in order to detect different activities and associated physiological patterns. Next, the use of a scalable and energy-efficient data structure that can represent the detected signal shapes. Then, the use of a supervised classifier (i.e. Support Vector Machine) in parallel with a quantitative survey involving a dozen participants to achieve a deeper understanding of the influence of each collected metric and its use in detecting user activities and contexts. Finally, the use of novel representations to visualize the activities and social interactions of all the users, allowing the creation of quick and easy-to-understand comparisons. The tools used in this article are freely available online under a MIT licence.
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paper_title: From Uncertain Photos to Certain Coverage: a Novel Photo Selection Approach to Mobile Crowdsensing

paper_content:

Traditional mobile crowdsensing photo selection process focuses on selecting photos from participants to a server. The server may contain tons of photos for a certain area. A new problem is how to select a set of photos from the server to a smartphone user when the user requests to view an area (e.g., a hot spot). The challenge of the new problem is that the photo set should attain both photo coverage and view quality (e.g., with clear Points of Interest). However, contributions of these geo-tagged photos could be uncertain for a target area due to unavailable information of photo shooting direction and no reference photos. In this paper, we propose a novel and generic server-to-requester photo selection approach. Our approach leverages a utility measure to quantify the contribution of a photo set, where photos' spatial distribution and visual correlation are jointly exploited to evaluate their performance on photo coverage and view quality. Finding the photo set with the maximum utility is proven to be NP-hard. We then propose an approximation algorithm based on a greedy strategy with rigorous theoretical analysis. The effectiveness of our approach is demonstrated with real-world datasets. The results show that the proposal outperforms other approaches with much higher photo coverage and better view quality.
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paper_title: Multi-task assignment for crowdsensing in mobile social networks

paper_content:

Mobile crowdsensing is a new paradigm in which a crowd of mobile users exploit their carried smart devices to conduct complex computation and sensing tasks in mobile social networks (MSNs). In this paper, we focus on the task assignment problem in mobile crowdsensing. Unlike traditional task scheduling problems, the task assignment in mobile crowdsensing must follow the mobility model of users in MSNs. To solve this problem, we propose an oFfline Task Assignment (FTA) algorithm and an oNline Task Assignment (NTA) algorithm. Both FTA and NTA adopt a greedy task assignment strategy. Moreover, we prove that the FTA algorithm is an optimal offline task assignment algorithm, and give a competitive ratio of the NTA algorithm. In addition, we demonstrate the significant performance of our algorithms through extensive simulations, based on four real MSN traces and a synthetic MSN trace.
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paper_title: Multidimensional context-aware social network architecture for mobile crowdsensing

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This article proposes a multidimensional context-aware social network architecture, which aims to provide a mobile ecosystem to enable context awareness in the development and utilization of mobile crowdsensing applications. This mobile ecosystem is constructed to provide context awareness capabilities for different roles (i.e., users or developers) in the system and facilitate interactions between them. This system can ease the development of context-aware mobile applications, and enable context-aware mobile crowdsensing considering environmental, personal, and social information. We present a flow of context-aware solutions designed on this system, and highlight the orchestrations and advantages of different context-aware schemes in the system for different types of users (requesters and participants) in mobile crowdsensing. We demonstrate the feasibility of the proposed mobile ecosystem by presenting a novel contextaware mobile crowdsensing application called Smart City, and evaluate the system performance based on this application.
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paper_title: CrowdTasker: Maximizing coverage quality in Piggyback Crowdsensing under budget constraint

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This paper proposes a novel task allocation framework, CrowdTasker, for mobile crowdsensing. CrowdTasker operates on top of energy-efficient Piggyback Crowdsensing (PCS) task model, and aims to maximize the coverage quality of the sensing task while satisfying the incentive budget constraint. In order to achieve this goal, CrowdTasker first predicts the call and mobility of mobile users based on their historical records. With a flexible incentive model and the prediction results, CrowdTasker then selects a set of users in each sensing cycle for PCS task participation, so that the resulting solution achieves near-maximal coverage quality without exceeding incentive budget. We evaluated CrowdTasker extensively using a large-scale real-world dataset and the results show that CrowdTasker significantly outperformed three baseline approaches by achieving 3%–60% higher coverage quality.
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paper_title: Mobile crowd sensing management with the ParticipAct living lab

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Abstract The widespread availability of smartphones have enabled the blossom of Mobile Crowd Sensing (MCS) projects, whose goal is to involve users in participatory tasks aimed at building large real-world datasets. In this framework, we present the large-scale experience of the ParticipAct Living Lab, an ongoing experiment at the University of Bologna, which involves about 170 students in MCS campaigns. Specifically, we originally present the analysis of the large set of ParticipAct collected results against some primary datasets in the literature; we present the evaluation and assessment of the original participatory sensing campaign management aspects of ParticipAct; and we report the lessons learned from this wide-scale deployment experience.
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paper_title: Matador: Mobile task detector for context-aware crowd-sensing campaigns

paper_content:

Ubiquity of internet-connected media- and sensor-equipped portable devices is enabling a new class of applications which exploit the power of crowds to perform sensing tasks in the real world. Such paradigm is referred as crowd-sensing, and lies at the intersection of crowd-sourcing and participatory sensing. This has a wide range of potential applications such as direct involvement of citizens into public decision making. In this work we present Matador, a framework to embed context-awareness in the presentation and execution of crowd-sensing tasks. This allows to present the right tasks, to the right users in the right circumstances, and to preserve normal device functioning. We present the design and prototype implementation of the platform, including an energy-efficient context sampling algorithm. We validate the proposed approach through a numerical study and a small pilot, and demonstrate the ability of the proposed system to efficiently deliver crowd-sensing tasks, while minimizing the consumption of mobile device resources.
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paper_title: Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN

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Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper.
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paper_title: Medusa: a programming framework for crowd-sensing applications

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The ubiquity of smartphones and their on-board sensing capabilities motivates crowd-sensing, a capability that harnesses the power of crowds to collect sensor data from a large number of mobile phone users. Unlike previous work on wireless sensing, crowd-sensing poses several novel requirements: support for humans-in-the-loop to trigger sensing actions or review results, the need for incentives, as well as privacy and security. Beyond existing crowd-sourcing systems, crowd-sensing exploits sensing and processing capabilities of mobile devices. In this paper, we design and implement Medusa, a novel programming framework for crowd-sensing that satisfies these requirements. Medusa provides high-level abstractions for specifying the steps required to complete a crowd-sensing task, and employs a distributed runtime system that coordinates the execution of these tasks between smartphones and a cluster on the cloud. We have implemented ten crowd-sensing tasks on a prototype of Medusa. We find that Medusa task descriptions are two orders of magnitude smaller than standalone systems required to implement those crowd-sensing tasks, and the runtime has low overhead and is robust to dynamics and resource attacks.
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paper_title: Reality mining: sensing complex social systems

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We introduce a system for sensing complex social systems with data collected from 100 mobile phones over the course of 9 months. We demonstrate the ability to use standard Bluetooth-enabled mobile telephones to measure information access and use in different contexts, recognize social patterns in daily user activity, infer relationships, identify socially significant locations, and model organizational rhythms.
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paper_title: Evaluation of Simulation Engines for Crowdsensing Activities

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The goal of this paper is to analyze existing simulation engines and assess how well-suited they are for simulating the formation, existence and dissolution of dynamic social networks, with a special emphasis on networks formed around crowdsensing efforts. The crowd in this context is a loosely-coupled social network of people, who use their mobile devices to collect and share data and receive some sort of service or satisfaction in return. Often it is hard to predict whether users would like a certain future crowdsensing application, therefore it is necessary to simulate the expected behavior of the crowd in a pre-specified simulation environment. This paper proposes an urban parking scenario, in which the drivers collect and share parking related events. The main part of this research is the analysis of three simulation engines, which will show which is the best suited for simulating dynamic social networks formed around crowdsensing efforts. The results will show that there are generic simulation environments capable of simulating large crowds, which also possess suitable visualization tools and integration with geospatial data.
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paper_title: Social-Aware Device-to-Device Offloading Based on Experimental Mobility and Content Similarity Models

paper_content:

Device-to-device (D2D) offloading has been shown to be a highly effective technique to enhance the performance of wireless networks. Yet, for any two mobile users to share data efficiently and reliably via D2D links, they should be in close proximity for long enough period of time, share similar content interests, and have some level of incentive and trust to cooperate. In this work, we focus on the practical implementation aspects of D2D data sharing taking into account realistic operational conditions. To this end, we design and conduct an experimental study to collect location and neighbor discovery data from 38 mobile users in a university campus over several weeks using our own customized crowdsourcing Android mobile application. The collected data is then processed and utilized to empirically model mobility-related parameters that include contact frequency, contact duration, and inter-contact duration. The participating users did also fill a user interest survey in order to correlate mobility and connectivity patterns with content interests and social network relations. The obtained insights are then used to develop a practical implementation framework for designing effective D2D data sharing strategies. To test the proposed ideas under realistic operational constraints, we design and implement a social-aware D2D data sharing Android mobile application and demonstrate its functionality and effectiveness using an example case study scenario.
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paper_title: CrowdSenSim: a Simulation Platform for Mobile Crowdsensing in Realistic Urban Environments

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Smart cities take advantage of recent Information and Communication Technology (ICT) developments to provide added value to existing public services and improve quality of life for the citizens. The Internet of Things (IoT) paradigm makes the Internet more pervasive where objects equipped with computing, storage, and sensing capabilities are interconnected with communication technologies. Because of the widespread diffusion of IoT devices, applying the IoT paradigm to smart cities is an excellent solution to build sustainable ICT platforms. Having citizens involved in the process through mobile crowdsensing (MCS) techniques augments capabilities of these ICT platforms without additional costs. For proper operation, MCS systems require the contribution from a large number of participants. Simulations are therefore a candidate tool to assess the performance of MCS systems. In this paper, we illustrate the design of CrowdSenSim, a simulator for mobile crowdsensing. CrowdSenSim is designed specifically for realistic urban environments and smart cities services. We demonstrate the effectiveness of CrowdSenSim for the most popular MCS sensing paradigms (participatory and opportunistic), and we present its applicability using a smart public street lighting scenario.
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paper_title: The participact mobile crowd sensing living lab: The testbed for smart cities

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In recent years, the widespread availability of smartphones provided with sensors has enabled the possibility of harvesting large quantities of data in urban areas exploiting user devices, thus enabling so-called mobile crowd sensing (MCS). While many efforts have been made to improve specific techniques for MCS - spanning from signal processing to the assignment of data collection campaigns to users, and to the entire data processing spectrum - to the best of our knowledge, thus far there have been no active experiments of MCS that involve all these techniques in a large-scale real-world experiment. Based on these considerations, we started the ParticipAct Living Lab testbed, an ongoing experiment at the University of Bologna involving 300 students for one year in crowd sensing campaigns that can passively access smartphone sensors and also require active user collaboration. In this article we describe the guidelines behind the design of ParticipAct, as well as its features and architecture. Moreover, we report some of the seminal results gathered during the first three months of its deployment, including accuracy of the classifier provided by ParticipAct client and user inclination to successfully complete tasks depending on the level of active collaboration required to executing them.
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paper_title: CupCarbon: a multi-agent and discrete event wireless sensor network design and simulation tool

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This paper presents the first version of a Wireless Sensor Network simulator, called CupCarbon. It is a multi-agent and discrete event Wireless Sensor Network (WSN) simulator. Networks can be designed and prototyped in an ergonomic user-friendly interface using the OpenStreetMap (OSM) framework by deploying sensors directly on the map. It can be used to study the behaviour of a network and its costs. The main objectives of CupCarbon are both educational and scientific. It can help trainers to explain the basic concepts and how sensor networks work and it can help scientists to test their wireless topologies, protocols, etc. The current version can be used only to study the power diagram of each sensor and the overall network. The power diagrams can be calculated and displayed as a function of the simulated time. Prototyping networks is more realistic compared to existing simulators.
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paper_title: Cost analysis of smart lighting solutions for smart cities

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Street lighting is an essential community service, but current implementations are not energy efficient and and require municipalities to spend up to 40% of their allocated budget. In this paper, we propose heuristics and devise a comparison methodology for new smart lighting solutions in next generation smart cities. The proposed smart lighting techniques make use of Internet of Things (IoT) augmented lampposts, which save energy by turning off or dimming the light in the absence of citizens nearby. Assessing costs and benefits in adopting the new smart lighting solutions is a pillar step for municipalities to foster real implementation. For evaluation purposes, we have developed a custom simulator which allows the deployment of lampposts in realistic urban environments. The citizens travel on foot along the streets and trigger activation of the lampposts according to the proposed heuristics. For the city of Luxembourg, the results highlight that replacing all existing lamps with LEDs and dimming light intensity in the absence of users in the vicinity of the lampposts is convenient and provides an economical return already after the first year of deployment.
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paper_title: PRISM: platform for remote sensing using smartphones

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To realize the potential of opportunistic and participatory sensing using mobile smartphones, a key challenge is ensuring the ease of developing and deploying such applications, without the need for the application writer to reinvent the wheel each time. To this end, we present a Platform for Remote Sensing using Smartphones (PRISM) that balances the interconnected goals of generality, security, and scalability. PRISM allows application writers to package their applications as executable binaries, which offers efficiency and also the flexibility of reusing existing code modules. PRISM then pushes the application out automatically to an appropriate set of phones based on a specified set of predicates. This push model enables timely and scalable application deployment while still ensuring a good degree of privacy. To safely execute untrusted applications on the smartphones, while allowing them controlled access to sensitive sensor data, we augment standard software sandboxing with several PRISM-specific elements like resource metering and forced amnesia. We present three applications built on our implementation of PRISM on Windows Mobile: citizen journalist, party thermometer, and road bump monitor. These applications vary in the set of sensors they use and in their mode of operation (depending on human input vs. automatic). We report on our experience from a small-scale deployment of these applications. We also present a large-scale simulation-based analysis of the scalability of PRISM's push model.
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paper_title: Towards rich mobile phone datasets: Lausanne data collection campaign

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Mobile phones have recently been used to collect large-scale continuous data about human behavior. In a paradigm known as people centric sensing, users are not only the carriers of sensing devices, but also the sources and consumers of sensed events. This paper describes a data collection campaign wherein Nokia N95 phones are allocated to a heterogeneous sample of nearly 170 participants from Lausanne, a mid-tier city in Switzerland, to be used over a period of one year. The data collection software runs on the background of the phones in a non-intrusive manner, yielding data on modalities such as social interaction and spatial behavior. The main motivations for organizing a new campaign on top of the ones that have been successfully conducted in the past are the following: First, in comparison to the Reality Mining data, generated in 2004-2005, the present data set is expected to provide a richer means to study location attributes, in particular, because today’s mobile phones are more powerful and equipped with more sensors. Second, we aim to recruit a heterogeneous set of participants, comprising family and leisure related social networks in addition to organizationally driven ones. This paper provides a methodological description of the project and shows the potential of the resulting data set in terms of illuminating multiple aspects of human behavior.
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paper_title: ParticipAct: A Large-Scale Crowdsensing Platform

paper_content:

In recent years, the widespread availability of sensor-provided smartphones has enabled the possibility of harvesting large quantities of data in urban areas exploiting user devices, so enabling the so-called crowdsensing that allows to realize complex applications impossible without the involvement of the research community. While many efforts have been made to improve specific techniques—spanning from signal processing to the assignment of data collection campaigns to users, and to the entire data processing—to the best of our knowledge, there are no active experiments aimed to explore the challenging issues raised by the management of large-scale crowdsensing campaigns as real-world experiments. This paper presents the ParticipAct platform and its ParticipAct living lab, an ongoing experiment at the University of Bologna that involves 170 students for one year in several crowdsensing campaigns that can access passively smartphone sensors and also prompt for user active collaboration. In this paper, we describe the guidelines behind the design of ParticipAct, its features, its architecture, and report quantitative results that assess and confirm the feasibility, obtained via intelligent coordination and management of crowdsensing campaigns.
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paper_title: Lowering the barriers to large-scale mobile crowdsensing

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Mobile crowdsensing is becoming a vital technique for environment monitoring, infrastructure management, and social computing. However, deploying mobile crowdsensing applications in large-scale environments is not a trivial task. It creates a tremendous burden on application developers as well as mobile users. In this paper we try to reveal the barriers hampering the scale-up of mobile crowdsensing applications, and to offer our initial thoughts on the potential solutions to lowering the barriers.
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paper_title: Simulation environment for investigating crowd-sensing based urban parking

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This paper introduces a simulation environment developed for analyzing crowd-sensing based applications in the Smart City application domain. As a case study, an urban parking application scenario is investigated and presented. In this scenario, smart citizens collect and share parking related events, such as leaving or occupying a free parking spot. These events can be presented on a real-time city map and used in navigation software, thereby helping others alleviate parking related issues, such as the time spent while cruising for parking. The simulation environment, implemented in Java, allows to investigate and assess the critical user base of crowd-sensing based smart city applications, and the requisites (benefits, challenges) of introducing such applications. Our simulation results show that considerable gain (approx. 15%) can be achieved in the cruising time even with relatively low (30%) user base in a medium size city (Novi Sad, Serbia). Moreover, the proposed simulation environment can be used also in real field measurements by replacing/extending input data from real users.
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paper_title: CRATER: A Crowd Sensing Application to Estimate Road Conditions

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We developed a crowd sensing application to estimate road conditions (CRATER). CRATER is a smartphone application that opportunistically measures acceleration when it finds itself on the road in order to map and measure the locations of potholes and speedbumps. It does not require input from users, but can report detected potholes and speedbumps to a cloud-hosted application engine, which stores partially processed data received from smartphones of participating users and jointly processes it to obtain a better estimate of road conditions. The information is published in map form on the web. This map allows both citizens and municipal authorities to localize potholes, road segments in need of repair, and imbalances in infrastructure maintenance efforts across cities. Road tests demonstrate that CRATER succeeds in correctly detecting roughly 90% of potholes and 95% of speedbumps, while generating false alarms only about 10% and 5% of the time, respectively.
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paper_title: Crowdsensing Simulation Using ns-3

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A crowdsensing network is a sensor network in which sensors are users that sense the environment and send the obtained data using, for instance, their smartphones. The performance of such sensor networks depends heavily on the mobility of the users and their willingness to collaborate. It is hard to obtain a stable set of users to evaluate such kinds of sensor networks and, for that reason, studies of crowdsensing networks are scarce. In this paper, we describe how the ns-3 network simulator can be used to simulate some crowdsensing networks with specific characteristics by using the mobility properties of network nodes together with the wireless interface in ad hoc network mode. We model the identification of network nodes with users of the crowdsensing network and we define how to simulate user sensing capabilities. Finally, we present a simulation example for a specific crowdsensing network where users report incidents in the public rail transport.
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paper_title: When Mobile Crowd Sensing Meets Traditional Industry

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With the evolution of mobile phone sensing and wireless networking technologies, mobile crowd sensing (MCS) has become a promising paradigm for large-scale sensing applications. MCS is a type of multi-participant sensing that has been widely used by many sensing applications because of its inherent capabilities, e.g., high mobility, scalability, and cost effectiveness. This paper reviews the existing works of MCS and clarifies the operability of MCS in sensing applications. With wide use and operability of MCS, MCS’s industrial applications are investigated based on the clarifications of: 1) the evolution of industrial sensing and 2) the benefits MCS can provide to current industrial sensing. As a feasible industrial sensing paradigm, MCS opens up a new field that provides a flexible, scalable, and cost-effective solution for addressing sensing problems in industrial spaces.
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paper_title: Delay-Sensitive Mobile Crowdsensing: Algorithm Design and Economics

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In a delay-sensitive mobile crowdsensing (MCS) platform, a service provider offers monetary incentives to mobile users for participating in the data collection and reporting their obtained data by a deadline . One aspect missing from most prior literature in the incentive mechanism design is the consideration of the detailed data reporting process through cellular or Wi-Fi networks. In this paper, we consider the interactions between the service provider and the users in two stages. First, the service provider chooses a reward to maximize its expected profit under the incomplete information of the users’ responses. Next, given the reward, each user makes his participation and reporting decisions, which are complicated due to his mobility and network heterogeneity. We propose an algorithm to compute the optimal user's decisions under the general setting using dynamic programming, and derive closed-form decision criteria for the special yet practical case of a non-discounted reward. We compute the optimal reward by characterizing the solution set and the discontinuity in the profit function. Simulation results show that our proposed algorithm achieves a significant gain in the user payoff over three benchmark heuristic schemes. In addition, a service provider's profit is sensitive to the estimation of the users’ Wi-Fi availabilities.
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paper_title: Dynamic Routing for Social Information Sharing

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Today, mobile users are intensively interconnected thanks to the emerging mobile social networks, where they share location-based information with each other when traveling on different routes and visit different areas of the city. In our model, the information collected is aggregated over all users’ trips and made publicly available as a public good. Due to information overlap, the total useful content amount increases with the diversity in path choices made by the users, and it is crucial to motivate selfish users to choose different paths, despite the potentially higher costs associated with their trips. In this paper, we combine the benefits from social information sharing with the fundamental routing problem where a unit mass of non-atomic selfish users decides their trips in a non-cooperative game by choosing between a high-cost and a low-cost path. To remedy the inefficient low-content equilibrium where all users choose to explore a single path (the low-cost path), we propose and analyze two new incentive mechanisms that can be used by the social network application, one based on side payments and the other on restricting access to content for users that choose the low cost path. Under asymmetric information about user types (their valuations for content quality), both mechanisms efficiently penalize the participants that use the low-cost path and reward the participants that take the high-cost path. They lead to greater path diversity and hence to more total available content at the social cost of reduced user participation or restricted content to part of the users. We show that user heterogeneity can have opposite effects on social efficiency depending on the mechanism used. We also obtain interesting price of anarchy results that show some fundamental tradeoffs between achieving path diversity and maintaining greater user participation, motivating a combined mechanism to further increase the social welfare. Our model extends classical dynamic routing in the case of externalities caused from traffic on different paths of the network.
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paper_title: Incentive mechanisms for smartphone collaboration in data acquisition and distributed computing

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This paper analyzes and compares different incentive mechanisms for a client to motivate the collaboration of smartphone users on both data acquisition and distributed computing applications.
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paper_title: Incentivizing Wi-Fi Network Crowdsourcing: A Contract Theoretic Approach

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Crowdsourced wireless community network enables individual users to share their private Wi-Fi access points (APs) with each other, hence can achieve a large Wi-Fi coverage with a small deployment cost via crowdsourcing. This paper presents a novel contract-based incentive framework to incentivize such a Wi-Fi network crowdsourcing under incomplete information (where each user has certain private information such as mobility pattern and Wi-Fi access quality). In the proposed framework, the network operator designs and offers a set of contract items to users, each consisting of a Wi-Fi access price (that a user can charge others for accessing his AP) and a subscription fee (that a user needs to pay the operator for joining the community). Different from the existing contracts in the literature, in our contract model each user's best choice depends not only on his private information but also on other users' choices. This greatly complicates the contract design, as the operator needs to analyze the equilibrium choices of all users, rather than the best choice of each single user. We first derive the feasible contract that guarantees the users' truthful information disclosure based on the equilibrium analysis of user choice, and then derive the optimal (and feasible) contract that yields the maximal profit for the operator. Our analysis shows that a user who provides a higher Wi-Fi access quality is more likely to choose a higher Wi-Fi access price and subscription fee, regardless of the user mobility pattern. Simulation results further show that when increasing the average Wi-Fi access quality of users, the operator can gain more profit, but (counter-intuitively) offer lower Wi-Fi access prices and subscription fees for users.
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paper_title: A hybrid pricing mechanism for data sharing in P2P-based mobile crowdsensing

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Mobile crowdsensing (MCS) is becoming more and more popular with the increasing demand for various sensory data in many wireless applications. In the traditional server-client MCS system, a central server is often required to handle massive sensory data (e.g., collecting data from users who sense and dispatching data to users who request), hence it may incur severe congestion and high operational cost. In this work, we introduce a peer-to-peer (P2P) based MCS system, where the sensory data is stored in user devices locally and shared among users in an P2P manner. Hence, it can effectively alleviate the burden on the server, by leveraging the communication, computation, and cache resources of massive user devices. We focus on the economic incentive issue arising in the sharing of data among users in such a system, that is, how to incentivize users to share their sensed data with others. To achieve this, we propose a data market, together with a hybrid pricing mechanism, for users to sell their sensed data to others. We first study how would users choose the best way of obtaining desired data (i.e., sensing by themselves or purchasing from others). Then we analyze the user behavior dynamics as well as the data market evolution, by using the evolutionary game theory. We further characterize the users' equilibrium behaviors as well as the market equilibrium, and analyze the stability of the obtained equilibrium.
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paper_title: Trading Data in the Crowd: Profit-Driven Data Acquisition for Mobile Crowdsensing

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As a significant business paradigm, data trading has attracted increasing attention. However, the study of data acquisition in data markets is still in its infancy. Mobile crowdsensing has been recognized as an efficient and scalable way to acquire large-scale data. Designing a practical data acquisition scheme for crowd-sensed data markets has to consider three major challenges: crowd-sensed data trading format determination, profit maximization with polynomial computational complexity, and payment minimization in strategic environments. In this paper, we jointly consider these design challenges, and propose VENUS, which is the first profit-driVEN data acqUiSition framework for crowd-sensed data markets. Specifically, VENUS consists of two complementary mechanisms: VENUS-PRO for profit maximization and VENUS-PAY for payment minimization. Given the expected payment for each of the data acquisition points, VENUS-PRO greedily selects the most “cost-efficient” data acquisition points to achieve a sub-optimal profit. To determine the minimum payment for each data acquisition point, we further design VENUS-PAY, which is a data procurement auction in Bayesian setting. Our theoretical analysis shows that VENUS-PAY can achieve both strategy-proofness and optimal expected payment. We evaluate VENUS on a public sensory data set, collected by Intel Research, Berkeley Laboratory. Our evaluation results show that VENUS-PRO approaches the optimal profit, and VENUS-PAY outperforms the canonical second-price reverse auction, in terms of total payment.
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paper_title: Scalable Mobile Crowdsensing via Peer-to-Peer Data Sharing

paper_content:

Mobile crowdsensing (MCS) is a new paradigm of sensing by taking advantage of the rich embedded sensors of mobile user devices. However, the traditional server-client MCS architecture often suffers from the high operational cost on the centralized server (e.g., for storing and processing massive data), hence the poor scalability. Peer-to-peer (P2P) data sharing can effectively reduce the server's cost by leveraging the user devices’ computation and storage resources. In this work, we propose a novel P2P-based MCS architecture, where the sensing data is saved and processed in user devices locally and shared among users in a P2P manner. To provide necessary incentives for users in such a system, we propose a quality-aware data sharing market , where the users who sense data can sell data to others who request data but not want to sense the data by themselves. We analyze the user behavior dynamics from the game-theoretic perspective, and characterize the existence and uniqueness of the game equilibrium. We further propose best response iterative algorithms to reach the equilibrium with provable convergence. Our simulations show that the P2P data sharing can greatly improve the social welfare, especially in the model with a high transmission cost and a low trading price.
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paper_title: Contract Theory Based Incentive Scheme for Mobile Crowd Sensing Networks

paper_content:

Mobile crowd sensing networks (MCSNs) have emerged as a promising paradigm to provide various sensing services. With the increasing number of mobile users, how to develop an effective scheme to provide the high-quality and secure sensing data becomes a new challenge. In this paper, we propose a contract theory based scheme to provide sensing service in MCSNs. At first, with the analysis of the interaction experience between the crowd sensing platform and mobile user, a trust scheme is introduced to guarantee the quality of sensing data by considering the direct trust and indirect trust. Next, according to the transaction between crowd sensing platform and mobile user, an optimal contract based on incentive scheme is designed to stimulate mobile users to participate in crowd sensing network, where the contract item can not only maximize the platform utility, but also satisfy individual rationality and incentive compatibility. Finally, the numerical results show that the proposal outperforms the conventional schemes.
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paper_title: NoiseTube: Measuring and mapping noise pollution with mobile phones

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In this paper we present a new approach for the assessment of noise pollution involving the general public. The goal of this project is to turn GPS-equipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment. Thus each user can contribute by sharing their geo-localised measurements and further personal annotation to produce a collective noise map.
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paper_title: Profit-maximizing incentive for participatory sensing

paper_content:

We design an incentive mechanism based on all-pay auctions for participatory sensing. The organizer (principal) aims to attract a high amount of contribution from participating users (agents) while at the same time lowering his payout, which we for- mulate as a profit-maximization problem. We use a contribution- dependent prize function in an environment that is specifically tailored to participatory sensing, namely incomplete information (with information asymmetry), risk-averse agents, and stochastic population. We derive the optimal prize function that induces the maximum profit for the principal, while satisfying strict individual rationality (i.e., strictly have incentive to participate at equilibrium) for both risk-neutral and weakly risk-averse agents. The thus induced profit is demonstrated to be higher than the maximum profit induced by constant (yet optimized) prize. We also show that our results are readily extensible to cases of risk- neutral agents and deterministic populations. Index Terms—Mechanism design, Bayesian game, all-pay auc- tion, perturbation analysis, network economics, crowdsensing.
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paper_title: LiveCompare: grocery bargain hunting through participatory sensing

paper_content:

Many consumers are misled into paying high prices due to the search costs associated with attaining price information [16]. The popularity of bargain-hunting web sites like Slickdeals.net, which boasts 2.5 million visitors per month, hints that many shoppers are indeed in search of tools to help them save money. We present LiveCompare, a system that leverages the ubiquity of mobile camera phones to allow for grocery bargain hunting through participatory sensing. We utilize two-dimensional barcode decoding to automatically identify grocery products, as well as localization techniques to automatically pinpoint store locations. We show that an incentive scheme is inherently ingrained into our query/response protocol, and we suggest self-regulating mechanisms for preserving data integrity. As a result, we demonstrate that money-saving price comparisons can be conducted among brick and mortar grocery stores without the explicit cooperation of the stores.
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paper_title: How long to wait?: predicting bus arrival time with mobile phone based participatory sensing

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The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.
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paper_title: Noisemap - real-time participatory noise maps

paper_content:

Noise pollution is a problem increasingly acknowledged ::: by authorities and governments around the globe. However, ::: creating noise maps with conventional methods is either inaccurate ::: or very expensive. To increase the spatial and temporal ::: data resolution a high number of sensors must be deployed. ::: In this paper we present results and prototypes based ::: on participatory sensing leading to accurate, real-time noise ::: maps. First we present NoiseMap a application currently ::: released for Android phones. NoiseMap gathers data on ::: loudness and transfers it to the open urban sensing platform ::: da sense. da sense allows users to access and control their ::: data, generate real-time noise maps and data graphs. Public ::: data is made available using either a web service or a ::: JavaScript API.
---
paper_title: Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing

paper_content:

Mobile phone sensing is a new paradigm which takes advantage of the pervasive smartphones to collect and analyze data beyond the scale of what was previously possible. In a mobile phone sensing system, the platform recruits smartphone users to provide sensing service. Existing mobile phone sensing applications and systems lack good incentive mechanisms that can attract more user participation. To address this issue, we design incentive mechanisms for mobile phone sensing. We consider two system models: the platform-centric model where the platform provides a reward shared by participating users, and the user-centric model where users have more control over the payment they will receive. For the platform-centric model, we design an incentive mechanism using a Stackelberg game, where the platform is the leader while the users are the followers. We show how to compute the unique Stackelberg Equilibrium, at which the utility of the platform is maximized, and none of the users can improve its utility by unilaterally deviating from its current strategy. For the user-centric model, we design an auction-based incentive mechanism, which is computationally efficient, individually rational, profitable, and truthful. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our incentive mechanisms.
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paper_title: Intelligent Gaming for Mobile Crowd-Sensing Participants to Acquire Trustworthy Big Data in the Internet of Things

paper_content:

In mobile crowd-sensing systems, the value of crowd-sensed big data can be increased by incentivizing the users appropriately. Since data acquisition is participatory, crowd-sensing systems face the challenge of data trustworthiness and truthfulness assurance in the presence of adversaries whose motivation can be either manipulating sensed data or collaborating unfaithfully with the motivation of maximizing their income. This paper proposes a game theoretic methodology to ensure trustworthiness in user recruitment in mobile crowd-sensing systems. The proposed methodology is a platform-centric framework that consists of three phases: user recruitment, collaborative decision making on trust scores, and badge rewarding. In the proposed framework, users are incentivized by running sub-game perfect equilibrium and gamification techniques. Through simulations, we show that approximately 50% and a minimum of 15% improvement can be achieved by the proposed methodology in terms of platform and user utility, respectively, when compared with fully distributed and user-centric trustworthy crowd-sensing.
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paper_title: EmotionSense: a mobile phones based adaptive platform for experimental social psychology research

paper_content:

Today's mobile phones represent a rich and powerful computing platform, given their sensing, processing and communication capabilities. Phones are also part of the everyday life of billions of people, and therefore represent an exceptionally suitable tool for conducting social and psychological experiments in an unobtrusive way. de the ability of sensing individual emotions as well as activities, verbal and proximity interactions among members of social groups. Moreover, the system is programmable by means of a declarative language that can be used to express adaptive rules to improve power saving. We evaluate a system prototype on Nokia Symbian phones by means of several small-scale experiments aimed at testing performance in terms of accuracy and power consumption. Finally, we present the results of real deployment where we study participants emotions and interactions. We cross-validate our measurements with the results obtained through questionnaires filled by the users, and the results presented in social psychological studies using traditional methods. In particular, we show how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.
---
paper_title: Automatic Collection of Fuel Prices from a Network of Mobile Cameras

paper_content:

It is an undeniable fact that people want information. Unfortunately, even in today's highly automated society, a lot of the information we desire is still manually collected. An example is fuel prices where websites providing fuel price information either send their workers out to manually collect the prices or depend on volunteers manually relaying the information. This paper proposes a novel application of wireless sensor networks to automatically collect fuel prices from camera images of road-side price board (billboard) of service (or gas) stations. Our system exploits the ubiquity of mobile phones that have cameras as well as users contributing and sharing data. In our proposed system, cameras of contributing users will be automatically triggered when they get close to a service station. These images will then be processed by computer vision algorithms to extract the fuel prices. In this paper, we will describe the system architecture and present results from our computer vision algorithms. Based on 52 images, our system achieves a hit rate of 92.3% for correctly detecting the fuel price board from the image background and reads the prices correctly in 87.7% of them. To the best of our knowledge, this is the first instance of a sensor network being used for collecting consumer pricing information.
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paper_title: Optimal incentive-driven design of participatory sensing systems

paper_content:

Participatory sensing has emerged as a novel paradigm for data collection and collective knowledge formation about a state or condition of interest, sometimes linked to a geographic area. In this paper, we address the problem of incentive mechanism design for data contributors for participatory sensing applications. The service provider receives service queries in an area from service requesters and initiates an auction for user participation. Upon request, each user reports its perceived cost per unit of amount of participation, which essentially maps to a requested amount of compensation for participation. The participation cost quantifies the dissatisfaction caused to user due to participation. This cost is considered to be private information for each device, as it strongly depends on various factors inherent to it, such as the energy cost for sensing, data processing and transmission to the closest point of wireless access, the residual battery level, the number of concurrent jobs at the device processor, the required bandwidth to transmit data and the related charges of the mobile network operator, or even the user discomfort due to manual effort to submit data. Hence, participants have strong motive to mis-report their cost, i.e. declare a higher cost that the actual one, so as to obtain higher payment. We seek a mechanism for user participation level determination and payment allocation which is most viable for the provider, that is, it minimizes the total cost of compensating participants, while delivering a certain quality of experience to service requesters. We cast the problem in the context of optimal reverse auction design, and we show how the different quality of submitted information by participants can be tracked by the service provider and used in the participation level and payment selection procedures. We derive a mechanism that optimally solves the problem above, and at the same time it is individually rational (i.e., it motivates users to participate) and incentive-compatible (i.e. it motivates truthful cost reporting by participants). Finally, a representative participatory sensing case study involving parameter estimation is presented, which exemplifies the incentive mechanism above.
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paper_title: Incentive Mechanisms for Mobile Crowd Sensing: Current States and Challenges of Work

paper_content:

Mobile crowd sensing (MCS) is a new paradigm which leverages the ubiquity of sensor-equipped mobile devices such as smartphones, music players, and in-vehicle sensors at the edge of the Internet, to collect data. The new paradigm will fuel the evolution of the Internet of Things to three changes as follows: First, the terminal devices at the edge of the Internet change from PCs to mobile phones. Second, the interactive mode extends from the virtual space to the real physical world. Thirdly, the forwarding manner of sensing data are undergoing the transition from the priori to the opportunistic. To better meet the demands of MCS applications at a societal scale, incentive mechanisms are indispensable. In this paper, we will first overview three categories of MCS applications, and then propose a new architecture for MCS applications. Based on the architecture, we discuss various research challenges about incentive mechanism designs for MCS applications, followed by potential future work discussions. Finally, we present potential future works.
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paper_title: Incentives for Mobile Crowd Sensing: A Survey

paper_content:

Recent years have witnessed the fast proliferation of mobile devices (e.g., smartphones and wearable devices) in people's lives. In addition, these devices possess powerful computation and communication capabilities and are equipped with various built-in functional sensors. The large quantity and advanced functionalities of mobile devices have created a new interface between human beings and environments. Many mobile crowd sensing applications have thus been designed which recruit normal users to contribute their resources for sensing tasks. To guarantee good performance of such applications, it's essential to recruit sufficient participants. Thus, how to effectively and efficiently motivate normal users draws growing attention in the research community. This paper surveys diverse strategies that are proposed in the literature to provide incentives for stimulating users to participate in mobile crowd sensing applications. The incentives are divided into three categories: entertainment, service, and money. Entertainment means that sensing tasks are turned into playable games to attract participants. Incentives of service exchanging are inspired by the principle of mutual benefits. Monetary incentives give participants payments for their contributions. We describe literature works of each type comprehensively and summarize them in a compact form. Further challenges and promising future directions concerning incentive mechanism design are also discussed.
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paper_title: Budget-Feasible Online Incentive Mechanisms for Crowdsourcing Tasks Truthfully

paper_content:

Mobile crowd sensing (MCS) is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous novel applications. To achieve good service quality for an MCS application, incentive mechanisms are necessary to attract more user participation. Most existing mechanisms apply only for the offline scenario where all users report their strategic types in advance. On the contrary, we focus on a more realistic scenario where users arrive one by one online in a random order. Based on the online auction model, we investigate the problem that users submit their private types to the crowdsourcer when arriving, and the crowdsourcer aims at selecting a subset of users before a specified deadline for maximizing the value of services (assumed to be a nonnegative monotone submodular function) provided by selected users under a budget constraint. We design two online mechanisms, OMZ and OMG, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty, and constant competitiveness under the zero arrival-departure interval case and a more general case, respectively. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.
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paper_title: A preliminary investigation of user incentives to leverage crowdsensing activities

paper_content:

With the continuous emergence of smart portable devices, like TabletPC or smartphones, crowdsensing is becoming an active research topic. While exploiting the wisdom of the crowd provides huge benefits to pervasive applications, one of the key challenges remains to motivate people to contribute to flow of data by sharing some of the capabilities of their device. In this paper, we therefore report on the APISENSE platform, a participative platform that help scientists to collect realistic datasets from a population of voluntary participants. More specifically, we provide a preliminary investigation of how user incentives can be integrated in such a platform to encourage people to contribute while leaving enough flexibility to the scientist for choosing the model that fits their requirements.
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paper_title: Development of Mobile Radiation Monitoring System Utilizing Smartphone and Its Field Tests in Fukushima

paper_content:

We developed a series of inexpensive but accurate and mobile radiation detectors, which we named Pocket Geiger (POKEGA), to address the desire of ordinary people to own a radiation detector following the March 2011 Daiichi Nuclear Power Plant accidents in Fukushima, Japan. To reduce costs while maintaining accuracy and flexibility, we used a combination of a p-i-n photodiode detector connected to a smartphone via a microphone cable. The detector circuit design is optimized for simplicity and low cost, whereas the smartphone software application is tasked with handling the complex processing required. Furthermore, the device also used the GPS and networking capabilities of the smartphone for logging and data sharing. The 137Cs measurement range for a POKEGA-equipped smartphone is approximately from 0.05 to 10 mSv/h, which covers most radiation levels measured in Japan. Approximately 12000 POKEGA units were shipped in the six months following its release, and 2000 users have joined a Facebook community where they report measurement results and discuss hardware and software improvements. In parallel, we have addressed practical problems for POKEGA, such as vibration noise, energy consumption, and operating temperature, by conducting field tests in the Fukushima evacuation zone. The POKEGA series has been improved by solving such issues. This article reports on a new style of pragmatic sensor networking methodology, from the aspects of emergency response engineering, open-sourced development, and consumer-generated measurements.
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paper_title: Common Sense: participatory urban sensing using a network of handheld air quality monitors

paper_content:

Poor air quality is a global health issue, causing serious problems like asthma, cancer, and heart disease around the world. Earlier this decade, the World Health Organization estimated that three million people die each year from the effects of air pollution [6]. Unfortunately, while variations in air quality are significant, today's air quality monitors are very sparsely deployed. To address this visibility gap, the Common Sense project is developing participatory sensing systems that allow individuals to measure their personal exposure, groups to aggregate their members' exposure, and activists to mobilize grassroots community action.
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paper_title: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

paper_content:

We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.
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paper_title: NoiseTube: Measuring and mapping noise pollution with mobile phones

paper_content:

In this paper we present a new approach for the assessment of noise pollution involving the general public. The goal of this project is to turn GPS-equipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment. Thus each user can contribute by sharing their geo-localised measurements and further personal annotation to produce a collective noise map.
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paper_title: Urban WiFi characterization via mobile crowdsensing

paper_content:

We present a mobile crowdsensing approach for urban WiFi characterization that leverages commodity smartphones and the natural mobility of people. Specifically, we report measurement results obtained for Edinburgh, a representative European city, on detecting the presence of deployedWiFi APs via the mobile crowdsensing approach. They show that few channels in 2.4GHz are heavily used; in contrast, there is hardly any activity in the 5GHz band even though relatively it has a greater number of available channels. Spatial analysis of spectrum usage reveals that mutual interference among nearby APs operating in the same channel can be a serious problem with around 10 APs contending with each other in many locations. We find that the characteristics of WiFi deployments at city-scale are similar to that of WiFi deployments in public spaces of different indoor environments. We validate our approach in comparison with wardriving, and also show that our findings generally match with previous studies based on other measurement approaches. As an application of the mobile crowdsensing based urban WiFi monitoring, we outline a cloud based WiFi router configuration service for better interference management with global awareness in urban areas.
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paper_title: Micro-Blog: sharing and querying content through mobile phones and social participation

paper_content:

Recent years have witnessed the impacts of distributed content sharing (Wikipedia, Blogger), social networks (Facebook, MySpace), sensor networks, and pervasive computing. We believe that significant more impact is latent in the convergence of these ideas on the mobile phone platform. Phones can be envisioned as people-centric sensors capable of aggregating participatory as well as sensory inputs from local surroundings. The inputs can be visualized in different dimensions, such as space and time. When plugged into the Internet, the collaborative inputs from phones may enable a high resolution view of the world. This paper presents the architecture and implementation of one such system, called Micro-Blog. New kinds of application-driven challenges are identified and addressed in the context of this system. Implemented on Nokia N95 mobile phones, Micro-Blog was distributed to volunteers for real life use. Promising feedback suggests that Micro-Blog can be a deployable tool for sharing, browsing, and querying global information.
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paper_title: LiveCompare: grocery bargain hunting through participatory sensing

paper_content:

Many consumers are misled into paying high prices due to the search costs associated with attaining price information [16]. The popularity of bargain-hunting web sites like Slickdeals.net, which boasts 2.5 million visitors per month, hints that many shoppers are indeed in search of tools to help them save money. We present LiveCompare, a system that leverages the ubiquity of mobile camera phones to allow for grocery bargain hunting through participatory sensing. We utilize two-dimensional barcode decoding to automatically identify grocery products, as well as localization techniques to automatically pinpoint store locations. We show that an incentive scheme is inherently ingrained into our query/response protocol, and we suggest self-regulating mechanisms for preserving data integrity. As a result, we demonstrate that money-saving price comparisons can be conducted among brick and mortar grocery stores without the explicit cooperation of the stores.
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paper_title: Social serendipity: mobilizing social software

paper_content:

Many mobile devices incorporate low-power wireless connectivity protocols, such as Bluetooth, that can be used to identify an individual to other people nearby. We have developed an architecture that leverages this functionality in mobile phones - originally designed for communication at a distance - to connect people across the room. Serendipity is an application of the architecture. It combines the existing communications infrastructure with online introduction systems' functionality to facilitate interactions between physically proximate people through a centralized server. A new mobile-phone-based system uses Bluetooth hardware addresses and a database of user profiles to cue informal, face-to-face interactions between nearby users who don't know each other, but probably should.
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paper_title: Darwin phones: the evolution of sensing and inference on mobile phones

paper_content:

We present Darwin, an enabling technology for mobile phone sensing that combines collaborative sensing and classification techniques to reason about human behavior and context on mobile phones. Darwin advances mobile phone sensing through the deployment of efficient but sophisticated machine learning techniques specifically designed to run directly on sensor-enabled mobile phones (i.e., smartphones). Darwin tackles three key sensing and inference challenges that are barriers to mass-scale adoption of mobile phone sensing applications: (i) the human-burden of training classifiers, (ii) the ability to perform reliably in different environments (e.g., indoor, outdoor) and (iii) the ability to scale to a large number of phones without jeopardizing the "phone experience" (e.g., usability and battery lifetime). Darwin is a collaborative reasoning framework built on three concepts: classifier/model evolution, model pooling, and collaborative inference. To the best of our knowledge Darwin is the first system that applies distributed machine learning techniques and collaborative inference concepts to mobile phones. We implement the Darwin system on the Nokia N97 and Apple iPhone. While Darwin represents a general framework applicable to a wide variety of emerging mobile sensing applications, we implement a speaker recognition application and an augmented reality application to evaluate the benefits of Darwin. We show experimental results from eight individuals carrying Nokia N97s and demonstrate that Darwin improves the reliability and scalability of the proof-of-concept speaker recognition application without additional burden to users.
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paper_title: How long to wait?: predicting bus arrival time with mobile phone based participatory sensing

paper_content:

The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.
---
paper_title: Noisemap - real-time participatory noise maps

paper_content:

Noise pollution is a problem increasingly acknowledged ::: by authorities and governments around the globe. However, ::: creating noise maps with conventional methods is either inaccurate ::: or very expensive. To increase the spatial and temporal ::: data resolution a high number of sensors must be deployed. ::: In this paper we present results and prototypes based ::: on participatory sensing leading to accurate, real-time noise ::: maps. First we present NoiseMap a application currently ::: released for Android phones. NoiseMap gathers data on ::: loudness and transfers it to the open urban sensing platform ::: da sense. da sense allows users to access and control their ::: data, generate real-time noise maps and data graphs. Public ::: data is made available using either a web service or a ::: JavaScript API.
---
paper_title: PEIR, the personal environmental impact report, as a platform for participatory sensing systems research

paper_content:

PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.
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paper_title: Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype

paper_content:

Imagers are an increasingly significant source of sensory observations about human activity and the urban environment. ImageScape is a software tool for processing, clustering, and browsing large sets of images. Implemented as a set of web services with an Adobe Flash-based user interface, it supports clustering by both image features and context tags, as well as re-tagging of images in the user interface. Though expected to be useful in many applications, ImageScape was designed as an analysis component of DietSense, a software system under development at UCLA to support (1) the use of mobile devices for automatic multimedia documentation of dietary choices with just-in-time annotation, (2) efficient post facto review of captured media by participants and researchers, and (3) easy authoring and dissemination of the automatic data collection protocols. A pilot study, in which participants ran software that enabled their phones to autonomously capture images of their plates during mealtime, was conducted using an early prototype of the DietSense system, and the resulting image set used in the creation of ImageScape. ImageScape will support two kinds of users within the DietSense application: The participants in dietary studies will have the ability to easily audit their images, while the recipients of the images, health care professionals managing studies and performing analysis, will be able to rapidly browse and annotate large sets of images.
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paper_title: WreckWatch : Automatic Traffic Accident Detection and Notification with Smartphones

paper_content:

Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles. This paper describes how smartphones, such as the iPhone and Google Android platforms, can automatically detect traffic accidents using accelerometers and acoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coordinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that combines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awareness to first responders, and (3) we provide empirical results demonstrating the efficacy of different approaches employed by smartphone accident detection systems to prevent false positives.
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paper_title: VTrack: accurate, energy-aware road traffic delay estimation using mobile phones

paper_content:

Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors---GPS, WiFi, and/or cellular triangulation---opens up the attractive possibility of using position samples from drivers' phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don't have GPS at all, the GPS sensor doesn't work in "urban canyons" (tall buildings and tunnels) or when the phone is inside a pocket, and the GPS on many phones is power-hungry and drains the battery quickly. In these cases, VTrack can use alternative, less energy-hungry but noisier sensors like WiFi to estimate both a user's trajectory and travel time along the route. VTrack uses a hidden Markov model (HMM)-based map matching scheme and travel time estimation method that interpolates sparse data to identify the most probable road segments driven by the user and to attribute travel times to those segments. We present experimental results from real drive data and WiFi access point sightings gathered from a deployment on several cars. We show that VTrack can tolerate significant noise and outages in these location estimates, and still successfully identify delay-prone segments, and provide accurate enough delays for delay-aware routing algorithms. We also study the best sampling strategies for WiFi and GPS sensors for different energy cost regimes.
---
paper_title: EmotionSense: a mobile phones based adaptive platform for experimental social psychology research

paper_content:

Today's mobile phones represent a rich and powerful computing platform, given their sensing, processing and communication capabilities. Phones are also part of the everyday life of billions of people, and therefore represent an exceptionally suitable tool for conducting social and psychological experiments in an unobtrusive way. de the ability of sensing individual emotions as well as activities, verbal and proximity interactions among members of social groups. Moreover, the system is programmable by means of a declarative language that can be used to express adaptive rules to improve power saving. We evaluate a system prototype on Nokia Symbian phones by means of several small-scale experiments aimed at testing performance in terms of accuracy and power consumption. Finally, we present the results of real deployment where we study participants emotions and interactions. We cross-validate our measurements with the results obtained through questionnaires filled by the users, and the results presented in social psychological studies using traditional methods. In particular, we show how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.
---
paper_title: Participatory Air Pollution Monitoring Using Smartphones

paper_content:

Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on o-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the eect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.
---
paper_title: Automatic Collection of Fuel Prices from a Network of Mobile Cameras

paper_content:

It is an undeniable fact that people want information. Unfortunately, even in today's highly automated society, a lot of the information we desire is still manually collected. An example is fuel prices where websites providing fuel price information either send their workers out to manually collect the prices or depend on volunteers manually relaying the information. This paper proposes a novel application of wireless sensor networks to automatically collect fuel prices from camera images of road-side price board (billboard) of service (or gas) stations. Our system exploits the ubiquity of mobile phones that have cameras as well as users contributing and sharing data. In our proposed system, cameras of contributing users will be automatically triggered when they get close to a service station. These images will then be processed by computer vision algorithms to extract the fuel prices. In this paper, we will describe the system architecture and present results from our computer vision algorithms. Based on 52 images, our system achieves a hit rate of 92.3% for correctly detecting the fuel price board from the image background and reads the prices correctly in 87.7% of them. To the best of our knowledge, this is the first instance of a sensor network being used for collecting consumer pricing information.
---
paper_title: HazeWatch: A participatory sensor system for monitoring air pollution in Sydney

paper_content:

This paper describes our experiences with building and deploying a low-cost participatory system for urban air pollution monitoring in Sydney. Though air pollution imposes significant health costs on the urban community globally, today's published data on pollution concentrations is spatially too sparse, and does not allow for sufficiently accurate estimation of exposures for (potentially mobile) individuals in order to make medical inferences. The HazeWatch project described in this paper uses several low-cost mobile sensor units attached to vehicles to measure air pollution concentrations, and users' mobile phones to tag and upload the data in real time. The greater spatial granularity of data thus collected enables creation of pollution maps of metropolitan Sydney viewable in real-time over the web, as well as personalized apps that show the individual's exposure history and allow for route planning to reduce future exposure. We share the insights we obtained from building and trialling such a system in Sydney, and highlight challenges that can be addressed collaboratively by groups developing similar systems world-wide.
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paper_title: SoundSense: scalable sound sensing for people-centric applications on mobile phones

paper_content:

Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sensing applications. Perhaps the most ubiquitous and unexploited sensor on mobile phones is the microphone - a powerful sensor that is capable of making sophisticated inferences about human activity, location, and social events from sound. In this paper, we exploit this untapped sensor not in the context of human communications but as an enabler of new sensing applications. We propose SoundSense, a scalable framework for modeling sound events on mobile phones. SoundSense is implemented on the Apple iPhone and represents the first general purpose sound sensing system specifically designed to work on resource limited phones. The architecture and algorithms are designed for scalability and Soundsense uses a combination of supervised and unsupervised learning techniques to classify both general sound types (e.g., music, voice) and discover novel sound events specific to individual users. The system runs solely on the mobile phone with no back-end interactions. Through implementation and evaluation of two proof of concept people-centric sensing applications, we demostrate that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives.
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paper_title: Mobile Social Networks: Architectures, Social Properties, and Key Research Challenges

paper_content:

Mobile social networks (MSNs) are mobile communication systems focusing not only on the behaviour but also on the social needs of the users. In a broader view all mobile systems used by people in their everyday lives can be characterized as MSNs, since all interactions taking place follow social rather than random patterns. Whether the deployed communication system takes into account the social background of the underlying network, depends on its form and capabilities. This article presents a review of the relevant work published in MSNs. Initially the basic architectures and components are summarized. Then the basic social properties of the network, as found in the key literature, are extensively examined. These properties are the main source of inspiration for new MSN protocols and applications, especially for non centralized systems. Finally the key research problems and the open issues in the area are presented, including future applications and privacy concerns.
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paper_title: WhozThat? evolving an ecosystem for context-aware mobile social networks

paper_content:

One of the most compelling social questions, which until now was left unanswered by current technology, is "Who's that?" This question is usually asked about a new unfamiliar person in a social setting who piques your curiosity. This article presents WhozThat, a system that ties together online social networks with mobile smart- phones to answer this common and essential social question. WhozThat builds an infrastructure that shares social networking IDs locally, using wireless technology, while also leveraging a wireless connection to the Internet's social networks to bind identity with location. In addition, WhozThat offers an entire ecosystem on which increasingly complex context-aware applications can be built; that is, once the environment knows who one is, the environment can adapt its content based on the individual's identity or even the collective tastes of a local group. We describe a prototype of the basic WhozThat system and also describe a more advanced service we built, a context-aware music player. We also discuss security and privacy issues introduced by mobile social networks.
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paper_title: MobiClique: middleware for mobile social networking

paper_content:

We consider a mobile ad hoc network setting where Bluetooth enabled mobile devices communicate directly with other devices as they meet opportunistically. We design and implement a novel mobile social networking middleware named MobiClique. MobiClique forms and exploits ad hoc social networks to disseminate content using a store-carry-forward technique. Our approach distinguishes itself from other mobile social software by removing the need for a central server to conduct exchanges, by leveraging existing social networks to bootstrap the system, and by taking advantage of the social network overlay to disseminate content. We also propose an open API to encourage third-party application development. We discuss the system architecture and three example applications. We show experimentally that MobiClique successfully builds and maintains an ad hoc social network leveraging contact opportunities between friends and people sharing interest(s) for content exchanges. Our experience also provides insight into some of the key challenges and short-comings that researchers face when designing and deploying similar systems.
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paper_title: Understanding the coverage and scalability of place-centric crowdsensing

paper_content:

Crowd-enabled place-centric systems gather and reason over large mobile sensor datasets and target everyday user locations (such as stores, workplaces, and restaurants). Such systems are transforming various consumer services (for example, local search) and data-driven organizations (city planning). As the demand for these systems increases, our understanding of how to design and deploy successful crowdsensing systems must improve. In this paper, we present a systematic study of the coverage and scaling properties of place-centric crowdsensing. During a two-month deployment, we collected smartphone sensor data from 85 participants using a representative crowdsensing system that captures 48,000 different place visits. Our analysis of this dataset examines issues of core interest to place-centric crowdsensing, including place-temporal coverage, the relationship between the user population and coverage, privacy concerns, and the characterization of the collected data. Collectively, our findings provide valuable insights to guide the building of future place-centric crowdsensing systems and applications.
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paper_title: MoVi: mobile phone based video highlights via collaborative sensing

paper_content:

Sensor networks have been conventionally defined as a network of sensor motes that collaboratively detect events and report them to a remote monitoring station. This paper makes an attempt to extend this notion to the social context by using mobile phones as replacement of motes. We envision a social application where mobile phones collaboratively sense their ambience and recognize socially "interesting" events. The phone with a good view of the event triggers a video recording, and later, the video-clips from different phones are "stitched" into a video highlights of the occasion. We observe that such a video highlights is akin to the notion of event coverage in conventional sensor networks, only the notion of "event" has changed from physical to social. We have built a Mobile Phone based Video Highlights system (MoVi) using Nokia phones and iPod Nanos, and have experimented in real-life social gatherings. Results show that MoVi-generated video highlights (created offline) are quite similar to those created manually, (i.e., by painstakingly editing the entire video of the occasion). In that sense, MoVi can be viewed as a collaborative information distillation tool capable of filtering events of social relevance.
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paper_title: SPA: a smart phone assisted chronic illness self-management system with participatory sensing

paper_content:

The medical system has not been able to effectively adapt to the dramatic transformation in public health challenges; from acute to chronic and lifestyle-related illnesses. Although acute illnesses can be treated successfully in an office or hospital, chronic illnesses comprise the bulk of health care needs and require a very different approach. There is overwhelming consensus that the prevention and treatment of cardiovascular disease, diabetes, hypertension, chronic pain, obesity, asthma, HIV, and many other chronic illnesses require substantial patient self-management [2].
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paper_title: SociableSense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing

paper_content:

The interactions and social relations among users in workplaces have been studied by many generations of social psychologists. There is evidence that groups of users that interact more in workplaces are more productive. However, it is still hard for social scientists to capture fine-grained data about phenomena of this kind and to find the right means to facilitate interaction. It is also difficult for users to keep track of their level of sociability with colleagues. While mobile phones offer a fantastic platform for harvesting long term and fine grained data, they also pose challenges: battery power is limited and needs to be traded-off for sensor reading accuracy and data transmission, while energy costs in processing computationally intensive tasks are high. In this paper, we propose SociableSense, a smart phones based platform that captures user behavior in office environments, while providing the users with a quantitative measure of their sociability and that of colleagues. We tackle the technical challenges of building such a tool: the system provides an adaptive sampling mechanism as well as models to decide whether to perform computation of tasks, such as the execution of classification and inference algorithms, locally or remotely. We perform several micro-benchmark tests to fine-tune and evaluate the performance of these mechanisms and we show that the adaptive sampling and computation distribution schemes balance trade-offs among accuracy, energy, latency, and data traffic. Finally, by means of a social psychological study with ten participants for two working weeks, we demonstrate that SociableSense fosters interactions among the participants and helps in enhancing their sociability.
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paper_title: Ear-phone: an end-to-end participatory urban noise mapping system

paper_content:

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end participatory urban noise mapping system called Ear-Phone. Ear-Phone, for the first time, leverages Compressive Sensing to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95 and HP iPAQ mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. Extensive simulations and outdoor experiments demonstrate that Ear-Phone is a feasible platform to assess noise pollution, incurring reasonable system resource consumption at mobile devices and providing high reconstruction accuracy of the noise map.
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paper_title: Nericell: rich monitoring of road and traffic conditions using mobile smartphones

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We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.
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paper_title: Mobile crowd sensing in space weather monitoring: the mahali project

paper_content:

Space weather refers to the conditions and evolution of Earth's near space environment including electron density variations in the ionosphere. This environment is influenced by both the Sun and terrestrial processes, and has an impact on communications, navigation, and terrestrial power systems. The recent discovery of clear signatures in the ionosphere related to tsunamis and earthquakes suggests that the ionosphere itself may serve as a valuable and versatile sensor, registering many types of Earth- and space-based phenomena. To realize this potential, ionospheric electron density must be monitored through a dense wide-area sensor mesh that is expensive to realize with traditional deployments and observation techniques. Crowdsourcing can help pursue this novel direction by providing new capabilities, including an increase in the number of sensors as well as expanding data transport capabilities through participating devices that act as relays. This article describes the Mahali project, which is currently at the beginning of exploring these promising techniques. Mahali uses GPS signals that penetrate the ionosphere for science rather than positioning. A large number of ground-based sensors will be able to feed data through mobile devices into a cloud-based processing environment, enabling a tomographic analysis of the global ionosphere at unprecedented resolution and coverage. This novel approach brings the exploitation of the ionosphere as a global earth system sensor technologically and economically within reach.
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paper_title: Common Sense: participatory urban sensing using a network of handheld air quality monitors

paper_content:

Poor air quality is a global health issue, causing serious problems like asthma, cancer, and heart disease around the world. Earlier this decade, the World Health Organization estimated that three million people die each year from the effects of air pollution [6]. Unfortunately, while variations in air quality are significant, today's air quality monitors are very sparsely deployed. To address this visibility gap, the Common Sense project is developing participatory sensing systems that allow individuals to measure their personal exposure, groups to aggregate their members' exposure, and activists to mobilize grassroots community action.
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paper_title: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

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We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.
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paper_title: HealthAware: Tackling obesity with health aware smart phone systems

paper_content:

Obesity prevention requires individuals to have healthy eating and physical activity awareness in their daily lives. New technological products have been explored to help monitoring individual health behaviors. Smart phones are ubiq­uitous hand-held tools with rich embedded sensors and limited computing power. A smart phone is a prospective candidate to integrate physical exercise analysis and food intake monitoring. This paper presents a novel health-aware smart phone system (HealthAware) which utilizes the embedded accelerometer to monitor daily physical activities and the built-in camera to analyze food items. The system presents the user's physical activity counts at real time to remind how much daily activity is needed to keep healthy. The user takes pictures of food items and the system provides health related information regarding to the food item intakes. The system is composed of an on-device database which holds the user specific data and food item information. HealthAware is a real time practical system to prevent obesity by enhancing individual daily healthy behavior awareness.
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paper_title: Andwellness: An open mobile system for activity and experience sampling

paper_content:

Advances in mobile phone technology have allowed phones to become a convenient platform for real-time assessment of a participants health and behavior. AndWellness, a personal data collection system, uses mobile phones to collect and analyze data from both active, triggered user experience samples and passive logging of onboard environmental sensors. The system includes an application that runs on Android based mobile phones, server software that manages deployments and acts as a central repository for data, and a dashboard front end for both participants and researchers to visualize incoming data in real-time. Our system has gone through testing by researchers in preparation for deployment with participants, and we describe an initial qualitative study plus several planned future studies to demonstrate how our system can be used to better understand a user's health related habits and observations.
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paper_title: Sociability-Driven Framework for Data Acquisition in Mobile Crowdsensing Over Fog Computing Platforms for Smart Cities

paper_content:

Smart cities exploit the most advanced information technologies to improve and add value to existing public services. Having citizens involved in the process through mobile crowdsensing (MCS) augments the capabilities of the platform without enquiring additional costs. In this paper, we propose a novel framework for data acquisition in MCS deployed over a fog computing platform which facilitates a number of key operations including user recruitment and task completion. Proper data acquisition minimizes the monetary expenditure the platform sustains to recruit and compensate users as well as the energy they spend to sense and deliver data. We propose a new user recruitment policy called Distance, Sociability, Energy (DSE). This policy exploits three criteria: (i) spatial distance between users and tasks, (ii) user sociability, which is an estimate of the willingness of users to contribute to sensing tasks, and (iii) remaining battery charge of the devices. Performance evaluation is conducted in a real urban environment for a large number of participants with new metrics assessing the efficiency of recruitment and the accuracy of task completion. Results reveal that the average number of recruited users improves by nearly 20 percent if compared to policies using only spatial distance as selection criterion.
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paper_title: NoiseTube: Measuring and mapping noise pollution with mobile phones

paper_content:

In this paper we present a new approach for the assessment of noise pollution involving the general public. The goal of this project is to turn GPS-equipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment. Thus each user can contribute by sharing their geo-localised measurements and further personal annotation to produce a collective noise map.
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paper_title: Urban WiFi characterization via mobile crowdsensing

paper_content:

We present a mobile crowdsensing approach for urban WiFi characterization that leverages commodity smartphones and the natural mobility of people. Specifically, we report measurement results obtained for Edinburgh, a representative European city, on detecting the presence of deployedWiFi APs via the mobile crowdsensing approach. They show that few channels in 2.4GHz are heavily used; in contrast, there is hardly any activity in the 5GHz band even though relatively it has a greater number of available channels. Spatial analysis of spectrum usage reveals that mutual interference among nearby APs operating in the same channel can be a serious problem with around 10 APs contending with each other in many locations. We find that the characteristics of WiFi deployments at city-scale are similar to that of WiFi deployments in public spaces of different indoor environments. We validate our approach in comparison with wardriving, and also show that our findings generally match with previous studies based on other measurement approaches. As an application of the mobile crowdsensing based urban WiFi monitoring, we outline a cloud based WiFi router configuration service for better interference management with global awareness in urban areas.
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paper_title: Recruitment Framework for Participatory Sensing Data Collections

paper_content:

Mobile phones have evolved from devices that are just used for voice and text communication to platforms that are able to capture and transmit a range of data types (image, audio, and location). The adoption of these increasingly capable devices by society has enabled a potentially pervasive sensing paradigm - participatory sensing. A coordinated participatory sensing system engages individuals carrying mobile phones to explore phenomena of interest using in situ data collection. For participatory sensing to succeed, several technical challenges need to be solved. In this paper, we discuss one particular issue: developing a recruitment framework to enable organizers to identify well-suited participants for data collections based on geographic and temporal availability as well as participation habits. This recruitment system is evaluated through a series of pilot data collections where volunteers explored sustainable processes on a university campus.
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paper_title: Micro-Blog: sharing and querying content through mobile phones and social participation

paper_content:

Recent years have witnessed the impacts of distributed content sharing (Wikipedia, Blogger), social networks (Facebook, MySpace), sensor networks, and pervasive computing. We believe that significant more impact is latent in the convergence of these ideas on the mobile phone platform. Phones can be envisioned as people-centric sensors capable of aggregating participatory as well as sensory inputs from local surroundings. The inputs can be visualized in different dimensions, such as space and time. When plugged into the Internet, the collaborative inputs from phones may enable a high resolution view of the world. This paper presents the architecture and implementation of one such system, called Micro-Blog. New kinds of application-driven challenges are identified and addressed in the context of this system. Implemented on Nokia N95 mobile phones, Micro-Blog was distributed to volunteers for real life use. Promising feedback suggests that Micro-Blog can be a deployable tool for sharing, browsing, and querying global information.
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paper_title: LiveCompare: grocery bargain hunting through participatory sensing

paper_content:

Many consumers are misled into paying high prices due to the search costs associated with attaining price information [16]. The popularity of bargain-hunting web sites like Slickdeals.net, which boasts 2.5 million visitors per month, hints that many shoppers are indeed in search of tools to help them save money. We present LiveCompare, a system that leverages the ubiquity of mobile camera phones to allow for grocery bargain hunting through participatory sensing. We utilize two-dimensional barcode decoding to automatically identify grocery products, as well as localization techniques to automatically pinpoint store locations. We show that an incentive scheme is inherently ingrained into our query/response protocol, and we suggest self-regulating mechanisms for preserving data integrity. As a result, we demonstrate that money-saving price comparisons can be conducted among brick and mortar grocery stores without the explicit cooperation of the stores.
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paper_title: Social serendipity: mobilizing social software

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Many mobile devices incorporate low-power wireless connectivity protocols, such as Bluetooth, that can be used to identify an individual to other people nearby. We have developed an architecture that leverages this functionality in mobile phones - originally designed for communication at a distance - to connect people across the room. Serendipity is an application of the architecture. It combines the existing communications infrastructure with online introduction systems' functionality to facilitate interactions between physically proximate people through a centralized server. A new mobile-phone-based system uses Bluetooth hardware addresses and a database of user profiles to cue informal, face-to-face interactions between nearby users who don't know each other, but probably should.
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paper_title: Darwin phones: the evolution of sensing and inference on mobile phones

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We present Darwin, an enabling technology for mobile phone sensing that combines collaborative sensing and classification techniques to reason about human behavior and context on mobile phones. Darwin advances mobile phone sensing through the deployment of efficient but sophisticated machine learning techniques specifically designed to run directly on sensor-enabled mobile phones (i.e., smartphones). Darwin tackles three key sensing and inference challenges that are barriers to mass-scale adoption of mobile phone sensing applications: (i) the human-burden of training classifiers, (ii) the ability to perform reliably in different environments (e.g., indoor, outdoor) and (iii) the ability to scale to a large number of phones without jeopardizing the "phone experience" (e.g., usability and battery lifetime). Darwin is a collaborative reasoning framework built on three concepts: classifier/model evolution, model pooling, and collaborative inference. To the best of our knowledge Darwin is the first system that applies distributed machine learning techniques and collaborative inference concepts to mobile phones. We implement the Darwin system on the Nokia N97 and Apple iPhone. While Darwin represents a general framework applicable to a wide variety of emerging mobile sensing applications, we implement a speaker recognition application and an augmented reality application to evaluate the benefits of Darwin. We show experimental results from eight individuals carrying Nokia N97s and demonstrate that Darwin improves the reliability and scalability of the proof-of-concept speaker recognition application without additional burden to users.
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paper_title: How long to wait?: predicting bus arrival time with mobile phone based participatory sensing

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The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.
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paper_title: Noisemap - real-time participatory noise maps

paper_content:

Noise pollution is a problem increasingly acknowledged ::: by authorities and governments around the globe. However, ::: creating noise maps with conventional methods is either inaccurate ::: or very expensive. To increase the spatial and temporal ::: data resolution a high number of sensors must be deployed. ::: In this paper we present results and prototypes based ::: on participatory sensing leading to accurate, real-time noise ::: maps. First we present NoiseMap a application currently ::: released for Android phones. NoiseMap gathers data on ::: loudness and transfers it to the open urban sensing platform ::: da sense. da sense allows users to access and control their ::: data, generate real-time noise maps and data graphs. Public ::: data is made available using either a web service or a ::: JavaScript API.
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paper_title: PEIR, the personal environmental impact report, as a platform for participatory sensing systems research

paper_content:

PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.
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paper_title: Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype

paper_content:

Imagers are an increasingly significant source of sensory observations about human activity and the urban environment. ImageScape is a software tool for processing, clustering, and browsing large sets of images. Implemented as a set of web services with an Adobe Flash-based user interface, it supports clustering by both image features and context tags, as well as re-tagging of images in the user interface. Though expected to be useful in many applications, ImageScape was designed as an analysis component of DietSense, a software system under development at UCLA to support (1) the use of mobile devices for automatic multimedia documentation of dietary choices with just-in-time annotation, (2) efficient post facto review of captured media by participants and researchers, and (3) easy authoring and dissemination of the automatic data collection protocols. A pilot study, in which participants ran software that enabled their phones to autonomously capture images of their plates during mealtime, was conducted using an early prototype of the DietSense system, and the resulting image set used in the creation of ImageScape. ImageScape will support two kinds of users within the DietSense application: The participants in dietary studies will have the ability to easily audit their images, while the recipients of the images, health care professionals managing studies and performing analysis, will be able to rapidly browse and annotate large sets of images.
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paper_title: WreckWatch : Automatic Traffic Accident Detection and Notification with Smartphones

paper_content:

Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles. This paper describes how smartphones, such as the iPhone and Google Android platforms, can automatically detect traffic accidents using accelerometers and acoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coordinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that combines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awareness to first responders, and (3) we provide empirical results demonstrating the efficacy of different approaches employed by smartphone accident detection systems to prevent false positives.
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paper_title: VTrack: accurate, energy-aware road traffic delay estimation using mobile phones

paper_content:

Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors---GPS, WiFi, and/or cellular triangulation---opens up the attractive possibility of using position samples from drivers' phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don't have GPS at all, the GPS sensor doesn't work in "urban canyons" (tall buildings and tunnels) or when the phone is inside a pocket, and the GPS on many phones is power-hungry and drains the battery quickly. In these cases, VTrack can use alternative, less energy-hungry but noisier sensors like WiFi to estimate both a user's trajectory and travel time along the route. VTrack uses a hidden Markov model (HMM)-based map matching scheme and travel time estimation method that interpolates sparse data to identify the most probable road segments driven by the user and to attribute travel times to those segments. We present experimental results from real drive data and WiFi access point sightings gathered from a deployment on several cars. We show that VTrack can tolerate significant noise and outages in these location estimates, and still successfully identify delay-prone segments, and provide accurate enough delays for delay-aware routing algorithms. We also study the best sampling strategies for WiFi and GPS sensors for different energy cost regimes.
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paper_title: EmotionSense: a mobile phones based adaptive platform for experimental social psychology research

paper_content:

Today's mobile phones represent a rich and powerful computing platform, given their sensing, processing and communication capabilities. Phones are also part of the everyday life of billions of people, and therefore represent an exceptionally suitable tool for conducting social and psychological experiments in an unobtrusive way. de the ability of sensing individual emotions as well as activities, verbal and proximity interactions among members of social groups. Moreover, the system is programmable by means of a declarative language that can be used to express adaptive rules to improve power saving. We evaluate a system prototype on Nokia Symbian phones by means of several small-scale experiments aimed at testing performance in terms of accuracy and power consumption. Finally, we present the results of real deployment where we study participants emotions and interactions. We cross-validate our measurements with the results obtained through questionnaires filled by the users, and the results presented in social psychological studies using traditional methods. In particular, we show how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.
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paper_title: Participatory Air Pollution Monitoring Using Smartphones

paper_content:

Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on o-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the eect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.
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paper_title: Automatic Collection of Fuel Prices from a Network of Mobile Cameras

paper_content:

It is an undeniable fact that people want information. Unfortunately, even in today's highly automated society, a lot of the information we desire is still manually collected. An example is fuel prices where websites providing fuel price information either send their workers out to manually collect the prices or depend on volunteers manually relaying the information. This paper proposes a novel application of wireless sensor networks to automatically collect fuel prices from camera images of road-side price board (billboard) of service (or gas) stations. Our system exploits the ubiquity of mobile phones that have cameras as well as users contributing and sharing data. In our proposed system, cameras of contributing users will be automatically triggered when they get close to a service station. These images will then be processed by computer vision algorithms to extract the fuel prices. In this paper, we will describe the system architecture and present results from our computer vision algorithms. Based on 52 images, our system achieves a hit rate of 92.3% for correctly detecting the fuel price board from the image background and reads the prices correctly in 87.7% of them. To the best of our knowledge, this is the first instance of a sensor network being used for collecting consumer pricing information.
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paper_title: SoundSense: scalable sound sensing for people-centric applications on mobile phones

paper_content:

Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sensing applications. Perhaps the most ubiquitous and unexploited sensor on mobile phones is the microphone - a powerful sensor that is capable of making sophisticated inferences about human activity, location, and social events from sound. In this paper, we exploit this untapped sensor not in the context of human communications but as an enabler of new sensing applications. We propose SoundSense, a scalable framework for modeling sound events on mobile phones. SoundSense is implemented on the Apple iPhone and represents the first general purpose sound sensing system specifically designed to work on resource limited phones. The architecture and algorithms are designed for scalability and Soundsense uses a combination of supervised and unsupervised learning techniques to classify both general sound types (e.g., music, voice) and discover novel sound events specific to individual users. The system runs solely on the mobile phone with no back-end interactions. Through implementation and evaluation of two proof of concept people-centric sensing applications, we demostrate that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives.
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paper_title: WhozThat? evolving an ecosystem for context-aware mobile social networks

paper_content:

One of the most compelling social questions, which until now was left unanswered by current technology, is "Who's that?" This question is usually asked about a new unfamiliar person in a social setting who piques your curiosity. This article presents WhozThat, a system that ties together online social networks with mobile smart- phones to answer this common and essential social question. WhozThat builds an infrastructure that shares social networking IDs locally, using wireless technology, while also leveraging a wireless connection to the Internet's social networks to bind identity with location. In addition, WhozThat offers an entire ecosystem on which increasingly complex context-aware applications can be built; that is, once the environment knows who one is, the environment can adapt its content based on the individual's identity or even the collective tastes of a local group. We describe a prototype of the basic WhozThat system and also describe a more advanced service we built, a context-aware music player. We also discuss security and privacy issues introduced by mobile social networks.
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paper_title: MobiClique: middleware for mobile social networking

paper_content:

We consider a mobile ad hoc network setting where Bluetooth enabled mobile devices communicate directly with other devices as they meet opportunistically. We design and implement a novel mobile social networking middleware named MobiClique. MobiClique forms and exploits ad hoc social networks to disseminate content using a store-carry-forward technique. Our approach distinguishes itself from other mobile social software by removing the need for a central server to conduct exchanges, by leveraging existing social networks to bootstrap the system, and by taking advantage of the social network overlay to disseminate content. We also propose an open API to encourage third-party application development. We discuss the system architecture and three example applications. We show experimentally that MobiClique successfully builds and maintains an ad hoc social network leveraging contact opportunities between friends and people sharing interest(s) for content exchanges. Our experience also provides insight into some of the key challenges and short-comings that researchers face when designing and deploying similar systems.
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paper_title: SociableSense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing

paper_content:

The interactions and social relations among users in workplaces have been studied by many generations of social psychologists. There is evidence that groups of users that interact more in workplaces are more productive. However, it is still hard for social scientists to capture fine-grained data about phenomena of this kind and to find the right means to facilitate interaction. It is also difficult for users to keep track of their level of sociability with colleagues. While mobile phones offer a fantastic platform for harvesting long term and fine grained data, they also pose challenges: battery power is limited and needs to be traded-off for sensor reading accuracy and data transmission, while energy costs in processing computationally intensive tasks are high. In this paper, we propose SociableSense, a smart phones based platform that captures user behavior in office environments, while providing the users with a quantitative measure of their sociability and that of colleagues. We tackle the technical challenges of building such a tool: the system provides an adaptive sampling mechanism as well as models to decide whether to perform computation of tasks, such as the execution of classification and inference algorithms, locally or remotely. We perform several micro-benchmark tests to fine-tune and evaluate the performance of these mechanisms and we show that the adaptive sampling and computation distribution schemes balance trade-offs among accuracy, energy, latency, and data traffic. Finally, by means of a social psychological study with ten participants for two working weeks, we demonstrate that SociableSense fosters interactions among the participants and helps in enhancing their sociability.
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paper_title: Ear-phone: an end-to-end participatory urban noise mapping system

paper_content:

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end participatory urban noise mapping system called Ear-Phone. Ear-Phone, for the first time, leverages Compressive Sensing to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95 and HP iPAQ mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. Extensive simulations and outdoor experiments demonstrate that Ear-Phone is a feasible platform to assess noise pollution, incurring reasonable system resource consumption at mobile devices and providing high reconstruction accuracy of the noise map.
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paper_title: Nericell: rich monitoring of road and traffic conditions using mobile smartphones

paper_content:

We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.
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paper_title: Mobile crowd sensing in space weather monitoring: the mahali project

paper_content:

Space weather refers to the conditions and evolution of Earth's near space environment including electron density variations in the ionosphere. This environment is influenced by both the Sun and terrestrial processes, and has an impact on communications, navigation, and terrestrial power systems. The recent discovery of clear signatures in the ionosphere related to tsunamis and earthquakes suggests that the ionosphere itself may serve as a valuable and versatile sensor, registering many types of Earth- and space-based phenomena. To realize this potential, ionospheric electron density must be monitored through a dense wide-area sensor mesh that is expensive to realize with traditional deployments and observation techniques. Crowdsourcing can help pursue this novel direction by providing new capabilities, including an increase in the number of sensors as well as expanding data transport capabilities through participating devices that act as relays. This article describes the Mahali project, which is currently at the beginning of exploring these promising techniques. Mahali uses GPS signals that penetrate the ionosphere for science rather than positioning. A large number of ground-based sensors will be able to feed data through mobile devices into a cloud-based processing environment, enabling a tomographic analysis of the global ionosphere at unprecedented resolution and coverage. This novel approach brings the exploitation of the ionosphere as a global earth system sensor technologically and economically within reach.
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paper_title: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

paper_content:

We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.
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paper_title: HealthAware: Tackling obesity with health aware smart phone systems

paper_content:

Obesity prevention requires individuals to have healthy eating and physical activity awareness in their daily lives. New technological products have been explored to help monitoring individual health behaviors. Smart phones are ubiq­uitous hand-held tools with rich embedded sensors and limited computing power. A smart phone is a prospective candidate to integrate physical exercise analysis and food intake monitoring. This paper presents a novel health-aware smart phone system (HealthAware) which utilizes the embedded accelerometer to monitor daily physical activities and the built-in camera to analyze food items. The system presents the user's physical activity counts at real time to remind how much daily activity is needed to keep healthy. The user takes pictures of food items and the system provides health related information regarding to the food item intakes. The system is composed of an on-device database which holds the user specific data and food item information. HealthAware is a real time practical system to prevent obesity by enhancing individual daily healthy behavior awareness.
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paper_title: Andwellness: An open mobile system for activity and experience sampling

paper_content:

Advances in mobile phone technology have allowed phones to become a convenient platform for real-time assessment of a participants health and behavior. AndWellness, a personal data collection system, uses mobile phones to collect and analyze data from both active, triggered user experience samples and passive logging of onboard environmental sensors. The system includes an application that runs on Android based mobile phones, server software that manages deployments and acts as a central repository for data, and a dashboard front end for both participants and researchers to visualize incoming data in real-time. Our system has gone through testing by researchers in preparation for deployment with participants, and we describe an initial qualitative study plus several planned future studies to demonstrate how our system can be used to better understand a user's health related habits and observations.
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paper_title: Fostering participaction in smart cities: a geo-social crowdsensing platform

paper_content:

This article investigates how and to what extent the power of collective although imprecise intelligence can be employed in smart cities. The main visionary goal is to automate the organization of spontaneous and impromptu collaborations of large groups of people participating in collective actions (i.e., participAct), such as in the notable case of urban crowdsensing. In a crowdsensing environment, people or their mobile devices act as both sensors that collect urban data and actuators that take actions in the city, possibly upon request. Managing the crowdsensing process is a challenging task spanning several socio-technical issues: from the characterization of the regions under control to the quantification of the sensing density needed to obtain a certain accuracy; from the evaluation of a good balance between sensing accuracy and resource usage (number of people involved, network bandwidth, battery usage, etc.) to the selection of good incentives for people to participAct (monetary, social, etc.). To tackle these problems, this article proposes a crowdsensing platform with three main original technical aspects: an innovative geo-social model to profile users along different variables, such as time, location, social interaction, service usage, and human activities; a matching algorithm to autonomously choose people to involve in participActions and to quantify the performance of their sensing; and a new Android-based platform to collect sensing data from smart phones, automatically or with user help, and to deliver sensing/actuation tasks to users.
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paper_title: LiveCompare: grocery bargain hunting through participatory sensing

paper_content:

Many consumers are misled into paying high prices due to the search costs associated with attaining price information [16]. The popularity of bargain-hunting web sites like Slickdeals.net, which boasts 2.5 million visitors per month, hints that many shoppers are indeed in search of tools to help them save money. We present LiveCompare, a system that leverages the ubiquity of mobile camera phones to allow for grocery bargain hunting through participatory sensing. We utilize two-dimensional barcode decoding to automatically identify grocery products, as well as localization techniques to automatically pinpoint store locations. We show that an incentive scheme is inherently ingrained into our query/response protocol, and we suggest self-regulating mechanisms for preserving data integrity. As a result, we demonstrate that money-saving price comparisons can be conducted among brick and mortar grocery stores without the explicit cooperation of the stores.
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paper_title: Noisemap - real-time participatory noise maps

paper_content:

Noise pollution is a problem increasingly acknowledged ::: by authorities and governments around the globe. However, ::: creating noise maps with conventional methods is either inaccurate ::: or very expensive. To increase the spatial and temporal ::: data resolution a high number of sensors must be deployed. ::: In this paper we present results and prototypes based ::: on participatory sensing leading to accurate, real-time noise ::: maps. First we present NoiseMap a application currently ::: released for Android phones. NoiseMap gathers data on ::: loudness and transfers it to the open urban sensing platform ::: da sense. da sense allows users to access and control their ::: data, generate real-time noise maps and data graphs. Public ::: data is made available using either a web service or a ::: JavaScript API.
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paper_title: Edge Computing Architecture for Mobile Crowdsensing

paper_content:

Mobile crowdsensing (MCS) is a human-driven Internet of Things service empowering citizens to observe the phenomena of individual, community, or even societal value by sharing sensor data about their environment while on the move. Typical MCS service implementations utilize cloud-based centralized architectures, which consume a lot of computational resources and generate significant network traffic, both in mobile networks and toward cloud-based MCS services. Mobile edge computing (MEC) is a natural choice to distribute MCS solutions by moving computation to network edge, since an MEC-based architecture enables significant performance improvements due to the partitioning of problem space based on location, where real-time data processing and aggregation is performed close to data sources. This in turn reduces the associated traffic in mobile core and will facilitate MCS deployments of massive scale. This paper proposes an edge computing architecture adequate for massive scale MCS services by placing key MCS features within the reference MEC architecture. In addition to improved performance, the proposed architecture decreases privacy threats and permits citizens to control the flow of contributed sensor data. It is adequate for both data analytics and real-time MCS scenarios, in line with the 5G vision to integrate a huge number of devices and enable innovative applications requiring low network latency. Our analysis of service overhead introduced by distributed architecture and service reconfiguration at network edge performed on real user traces shows that this overhead is controllable and small compared with the aforementioned benefits. When enhanced by interoperability concepts, the proposed architecture creates an environment for the establishment of an MCS marketplace for bartering and trading of both raw sensor data and aggregated/processed information.
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paper_title: Does the Configuration of the Street Network Influence Where Outdoor Serious Violence Takes Place? Using Space Syntax to Test Crime Pattern Theory

paper_content:

Objectives ::: To examine the effect of the physical layout of the street network on the spatial distribution of outdoor serious violence. Crime pattern theory predicts crime would be more prevalent on more connected, accessible or traveled street segments, as these will be more likely to fall within an offender’s awareness space.
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paper_title: Evaluating the Reliability of Phones as Seismic Monitoring Instruments

paper_content:

Emergency responders must “see” the effects of an earthquake clearly and rapidly for effective response. This paper presents a novel use of cell phone and information technology to measure ground motion intensity parameters. The phone sensor is an imperfect device and has a limited operational range. Thus, shake table tests were performed to evaluate their reliability as seismic monitoring instruments. Representative handheld devices, either rigidly connected to the table or free to move, measured shaking intensity parameters well. Bias in 5%-damped spectral accelerations measured by phones was less than 0.05 and 0.2 [log(g)] during one-dimensional (1-D) and three-dimensional (3-D) shaking in frequencies ranging from 1 Hz to 10 Hz. They did tend to overestimate the Arias Intensity, but this error declined for stronger motions with larger signal-to-noise ratios. With these ubiquitous measurement devices, a more accurate and rapid portrayal of the damage distribution during an earthquake can be provided.
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paper_title: Common Sense: participatory urban sensing using a network of handheld air quality monitors

paper_content:

Poor air quality is a global health issue, causing serious problems like asthma, cancer, and heart disease around the world. Earlier this decade, the World Health Organization estimated that three million people die each year from the effects of air pollution [6]. Unfortunately, while variations in air quality are significant, today's air quality monitors are very sparsely deployed. To address this visibility gap, the Common Sense project is developing participatory sensing systems that allow individuals to measure their personal exposure, groups to aggregate their members' exposure, and activists to mobilize grassroots community action.
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paper_title: Real-Time Urban Monitoring Using Cell Phones: A Case Study in Rome

paper_content:

This paper describes a new real-time urban monitoring system. The system uses the Localizing and Handling Network Event Systems (LocHNESs) platform developed by Telecom Italia for the real-time evaluation of urban dynamics based on the anonymous monitoring of mobile cellular networks. In addition, data are supplemented based on the instantaneous positioning of buses and taxis to provide information about urban mobility in real time, ranging from traffic conditions to the movements of pedestrians throughout the city. This system was exhibited at the Tenth International Architecture Exhibition of the Venice Biennale. It marks the unprecedented monitoring of a large urban area, which covered most of the city of Rome, in real time using a variety of sensing systems and will hopefully open the way to a new paradigm of understanding and optimizing urban dynamics.
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paper_title: NoiseTube: Measuring and mapping noise pollution with mobile phones

paper_content:

In this paper we present a new approach for the assessment of noise pollution involving the general public. The goal of this project is to turn GPS-equipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment. Thus each user can contribute by sharing their geo-localised measurements and further personal annotation to produce a collective noise map.
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paper_title: Urban WiFi characterization via mobile crowdsensing

paper_content:

We present a mobile crowdsensing approach for urban WiFi characterization that leverages commodity smartphones and the natural mobility of people. Specifically, we report measurement results obtained for Edinburgh, a representative European city, on detecting the presence of deployedWiFi APs via the mobile crowdsensing approach. They show that few channels in 2.4GHz are heavily used; in contrast, there is hardly any activity in the 5GHz band even though relatively it has a greater number of available channels. Spatial analysis of spectrum usage reveals that mutual interference among nearby APs operating in the same channel can be a serious problem with around 10 APs contending with each other in many locations. We find that the characteristics of WiFi deployments at city-scale are similar to that of WiFi deployments in public spaces of different indoor environments. We validate our approach in comparison with wardriving, and also show that our findings generally match with previous studies based on other measurement approaches. As an application of the mobile crowdsensing based urban WiFi monitoring, we outline a cloud based WiFi router configuration service for better interference management with global awareness in urban areas.
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paper_title: Improving location reliability in crowd sensed data with minimal efforts

paper_content:

People-centric sensing with smart phones can be used for large scale sensing of the physical world by leveraging the cameras, microphones, GPSs, accelerometers, and other sensors on the phones. Ranging from manual photo tasks to automated sensing tasks for activity monitoring, any task can be crowd sourced to smart phones to sense data from different locations at reduced cost. However, the sensed data submitted by participants is not always reliable as they can submit false data to earn money without executing the actual task. Therefore, it is important to validate the sensed data. Validating the context of every sensed data point of each participant is not a scalable solution. One alternative is to first validate the location associated with the sensed data points in order to achieve a certain degree of reliability about the sensed data. However, location validation without support from the wireless carriers is difficult. To address this problem, we propose ILR, a scheme in which we Improve the Location Reliability of mobile crowd sensed data with minimal human efforts. In this scheme, we bootstrap the trust in the system by first manually or automatically using image processing techniques validating a small number of photos submitted by participants. Based on these validations, the location of these photos is assumed to be trusted. Second, we extend this location trust to co-located sensed data points found in the Bluetooth range of the devices that provided the validated photos. This transitive trust is extended until all the co-located tasks are trusted or no new data points are found. In addition, the scheme also helps to detect false location claims associated with sensed data. We applied ILR on data collected from our McSense prototype deployed on Android phones used by students on our campus and detected a significant percentage of the malicious users. Simulation results demonstrate that ILR works well at various densities and helps detect the false location claims based on a minimal numher of validations.
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paper_title: Micro-Blog: sharing and querying content through mobile phones and social participation

paper_content:

Recent years have witnessed the impacts of distributed content sharing (Wikipedia, Blogger), social networks (Facebook, MySpace), sensor networks, and pervasive computing. We believe that significant more impact is latent in the convergence of these ideas on the mobile phone platform. Phones can be envisioned as people-centric sensors capable of aggregating participatory as well as sensory inputs from local surroundings. The inputs can be visualized in different dimensions, such as space and time. When plugged into the Internet, the collaborative inputs from phones may enable a high resolution view of the world. This paper presents the architecture and implementation of one such system, called Micro-Blog. New kinds of application-driven challenges are identified and addressed in the context of this system. Implemented on Nokia N95 mobile phones, Micro-Blog was distributed to volunteers for real life use. Promising feedback suggests that Micro-Blog can be a deployable tool for sharing, browsing, and querying global information.
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paper_title: LiveCompare: grocery bargain hunting through participatory sensing

paper_content:

Many consumers are misled into paying high prices due to the search costs associated with attaining price information [16]. The popularity of bargain-hunting web sites like Slickdeals.net, which boasts 2.5 million visitors per month, hints that many shoppers are indeed in search of tools to help them save money. We present LiveCompare, a system that leverages the ubiquity of mobile camera phones to allow for grocery bargain hunting through participatory sensing. We utilize two-dimensional barcode decoding to automatically identify grocery products, as well as localization techniques to automatically pinpoint store locations. We show that an incentive scheme is inherently ingrained into our query/response protocol, and we suggest self-regulating mechanisms for preserving data integrity. As a result, we demonstrate that money-saving price comparisons can be conducted among brick and mortar grocery stores without the explicit cooperation of the stores.
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paper_title: Social serendipity: mobilizing social software

paper_content:

Many mobile devices incorporate low-power wireless connectivity protocols, such as Bluetooth, that can be used to identify an individual to other people nearby. We have developed an architecture that leverages this functionality in mobile phones - originally designed for communication at a distance - to connect people across the room. Serendipity is an application of the architecture. It combines the existing communications infrastructure with online introduction systems' functionality to facilitate interactions between physically proximate people through a centralized server. A new mobile-phone-based system uses Bluetooth hardware addresses and a database of user profiles to cue informal, face-to-face interactions between nearby users who don't know each other, but probably should.
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paper_title: Darwin phones: the evolution of sensing and inference on mobile phones

paper_content:

We present Darwin, an enabling technology for mobile phone sensing that combines collaborative sensing and classification techniques to reason about human behavior and context on mobile phones. Darwin advances mobile phone sensing through the deployment of efficient but sophisticated machine learning techniques specifically designed to run directly on sensor-enabled mobile phones (i.e., smartphones). Darwin tackles three key sensing and inference challenges that are barriers to mass-scale adoption of mobile phone sensing applications: (i) the human-burden of training classifiers, (ii) the ability to perform reliably in different environments (e.g., indoor, outdoor) and (iii) the ability to scale to a large number of phones without jeopardizing the "phone experience" (e.g., usability and battery lifetime). Darwin is a collaborative reasoning framework built on three concepts: classifier/model evolution, model pooling, and collaborative inference. To the best of our knowledge Darwin is the first system that applies distributed machine learning techniques and collaborative inference concepts to mobile phones. We implement the Darwin system on the Nokia N97 and Apple iPhone. While Darwin represents a general framework applicable to a wide variety of emerging mobile sensing applications, we implement a speaker recognition application and an augmented reality application to evaluate the benefits of Darwin. We show experimental results from eight individuals carrying Nokia N97s and demonstrate that Darwin improves the reliability and scalability of the proof-of-concept speaker recognition application without additional burden to users.
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paper_title: How long to wait?: predicting bus arrival time with mobile phone based participatory sensing

paper_content:

The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.
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paper_title: Noisemap - real-time participatory noise maps

paper_content:

Noise pollution is a problem increasingly acknowledged ::: by authorities and governments around the globe. However, ::: creating noise maps with conventional methods is either inaccurate ::: or very expensive. To increase the spatial and temporal ::: data resolution a high number of sensors must be deployed. ::: In this paper we present results and prototypes based ::: on participatory sensing leading to accurate, real-time noise ::: maps. First we present NoiseMap a application currently ::: released for Android phones. NoiseMap gathers data on ::: loudness and transfers it to the open urban sensing platform ::: da sense. da sense allows users to access and control their ::: data, generate real-time noise maps and data graphs. Public ::: data is made available using either a web service or a ::: JavaScript API.
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paper_title: WreckWatch : Automatic Traffic Accident Detection and Notification with Smartphones

paper_content:

Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles. This paper describes how smartphones, such as the iPhone and Google Android platforms, can automatically detect traffic accidents using accelerometers and acoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coordinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that combines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awareness to first responders, and (3) we provide empirical results demonstrating the efficacy of different approaches employed by smartphone accident detection systems to prevent false positives.
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paper_title: VTrack: accurate, energy-aware road traffic delay estimation using mobile phones

paper_content:

Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors---GPS, WiFi, and/or cellular triangulation---opens up the attractive possibility of using position samples from drivers' phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don't have GPS at all, the GPS sensor doesn't work in "urban canyons" (tall buildings and tunnels) or when the phone is inside a pocket, and the GPS on many phones is power-hungry and drains the battery quickly. In these cases, VTrack can use alternative, less energy-hungry but noisier sensors like WiFi to estimate both a user's trajectory and travel time along the route. VTrack uses a hidden Markov model (HMM)-based map matching scheme and travel time estimation method that interpolates sparse data to identify the most probable road segments driven by the user and to attribute travel times to those segments. We present experimental results from real drive data and WiFi access point sightings gathered from a deployment on several cars. We show that VTrack can tolerate significant noise and outages in these location estimates, and still successfully identify delay-prone segments, and provide accurate enough delays for delay-aware routing algorithms. We also study the best sampling strategies for WiFi and GPS sensors for different energy cost regimes.
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paper_title: EmotionSense: a mobile phones based adaptive platform for experimental social psychology research

paper_content:

Today's mobile phones represent a rich and powerful computing platform, given their sensing, processing and communication capabilities. Phones are also part of the everyday life of billions of people, and therefore represent an exceptionally suitable tool for conducting social and psychological experiments in an unobtrusive way. de the ability of sensing individual emotions as well as activities, verbal and proximity interactions among members of social groups. Moreover, the system is programmable by means of a declarative language that can be used to express adaptive rules to improve power saving. We evaluate a system prototype on Nokia Symbian phones by means of several small-scale experiments aimed at testing performance in terms of accuracy and power consumption. Finally, we present the results of real deployment where we study participants emotions and interactions. We cross-validate our measurements with the results obtained through questionnaires filled by the users, and the results presented in social psychological studies using traditional methods. In particular, we show how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.
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paper_title: Participatory Air Pollution Monitoring Using Smartphones

paper_content:

Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on o-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the eect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.
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paper_title: Automatic Collection of Fuel Prices from a Network of Mobile Cameras

paper_content:

It is an undeniable fact that people want information. Unfortunately, even in today's highly automated society, a lot of the information we desire is still manually collected. An example is fuel prices where websites providing fuel price information either send their workers out to manually collect the prices or depend on volunteers manually relaying the information. This paper proposes a novel application of wireless sensor networks to automatically collect fuel prices from camera images of road-side price board (billboard) of service (or gas) stations. Our system exploits the ubiquity of mobile phones that have cameras as well as users contributing and sharing data. In our proposed system, cameras of contributing users will be automatically triggered when they get close to a service station. These images will then be processed by computer vision algorithms to extract the fuel prices. In this paper, we will describe the system architecture and present results from our computer vision algorithms. Based on 52 images, our system achieves a hit rate of 92.3% for correctly detecting the fuel price board from the image background and reads the prices correctly in 87.7% of them. To the best of our knowledge, this is the first instance of a sensor network being used for collecting consumer pricing information.
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paper_title: HazeWatch: A participatory sensor system for monitoring air pollution in Sydney

paper_content:

This paper describes our experiences with building and deploying a low-cost participatory system for urban air pollution monitoring in Sydney. Though air pollution imposes significant health costs on the urban community globally, today's published data on pollution concentrations is spatially too sparse, and does not allow for sufficiently accurate estimation of exposures for (potentially mobile) individuals in order to make medical inferences. The HazeWatch project described in this paper uses several low-cost mobile sensor units attached to vehicles to measure air pollution concentrations, and users' mobile phones to tag and upload the data in real time. The greater spatial granularity of data thus collected enables creation of pollution maps of metropolitan Sydney viewable in real-time over the web, as well as personalized apps that show the individual's exposure history and allow for route planning to reduce future exposure. We share the insights we obtained from building and trialling such a system in Sydney, and highlight challenges that can be addressed collaboratively by groups developing similar systems world-wide.
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paper_title: SoundSense: scalable sound sensing for people-centric applications on mobile phones

paper_content:

Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sensing applications. Perhaps the most ubiquitous and unexploited sensor on mobile phones is the microphone - a powerful sensor that is capable of making sophisticated inferences about human activity, location, and social events from sound. In this paper, we exploit this untapped sensor not in the context of human communications but as an enabler of new sensing applications. We propose SoundSense, a scalable framework for modeling sound events on mobile phones. SoundSense is implemented on the Apple iPhone and represents the first general purpose sound sensing system specifically designed to work on resource limited phones. The architecture and algorithms are designed for scalability and Soundsense uses a combination of supervised and unsupervised learning techniques to classify both general sound types (e.g., music, voice) and discover novel sound events specific to individual users. The system runs solely on the mobile phone with no back-end interactions. Through implementation and evaluation of two proof of concept people-centric sensing applications, we demostrate that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives.
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paper_title: WhozThat? evolving an ecosystem for context-aware mobile social networks

paper_content:

One of the most compelling social questions, which until now was left unanswered by current technology, is "Who's that?" This question is usually asked about a new unfamiliar person in a social setting who piques your curiosity. This article presents WhozThat, a system that ties together online social networks with mobile smart- phones to answer this common and essential social question. WhozThat builds an infrastructure that shares social networking IDs locally, using wireless technology, while also leveraging a wireless connection to the Internet's social networks to bind identity with location. In addition, WhozThat offers an entire ecosystem on which increasingly complex context-aware applications can be built; that is, once the environment knows who one is, the environment can adapt its content based on the individual's identity or even the collective tastes of a local group. We describe a prototype of the basic WhozThat system and also describe a more advanced service we built, a context-aware music player. We also discuss security and privacy issues introduced by mobile social networks.
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paper_title: MobiClique: middleware for mobile social networking

paper_content:

We consider a mobile ad hoc network setting where Bluetooth enabled mobile devices communicate directly with other devices as they meet opportunistically. We design and implement a novel mobile social networking middleware named MobiClique. MobiClique forms and exploits ad hoc social networks to disseminate content using a store-carry-forward technique. Our approach distinguishes itself from other mobile social software by removing the need for a central server to conduct exchanges, by leveraging existing social networks to bootstrap the system, and by taking advantage of the social network overlay to disseminate content. We also propose an open API to encourage third-party application development. We discuss the system architecture and three example applications. We show experimentally that MobiClique successfully builds and maintains an ad hoc social network leveraging contact opportunities between friends and people sharing interest(s) for content exchanges. Our experience also provides insight into some of the key challenges and short-comings that researchers face when designing and deploying similar systems.
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paper_title: MoVi: mobile phone based video highlights via collaborative sensing

paper_content:

Sensor networks have been conventionally defined as a network of sensor motes that collaboratively detect events and report them to a remote monitoring station. This paper makes an attempt to extend this notion to the social context by using mobile phones as replacement of motes. We envision a social application where mobile phones collaboratively sense their ambience and recognize socially "interesting" events. The phone with a good view of the event triggers a video recording, and later, the video-clips from different phones are "stitched" into a video highlights of the occasion. We observe that such a video highlights is akin to the notion of event coverage in conventional sensor networks, only the notion of "event" has changed from physical to social. We have built a Mobile Phone based Video Highlights system (MoVi) using Nokia phones and iPod Nanos, and have experimented in real-life social gatherings. Results show that MoVi-generated video highlights (created offline) are quite similar to those created manually, (i.e., by painstakingly editing the entire video of the occasion). In that sense, MoVi can be viewed as a collaborative information distillation tool capable of filtering events of social relevance.
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paper_title: SPA: a smart phone assisted chronic illness self-management system with participatory sensing

paper_content:

The medical system has not been able to effectively adapt to the dramatic transformation in public health challenges; from acute to chronic and lifestyle-related illnesses. Although acute illnesses can be treated successfully in an office or hospital, chronic illnesses comprise the bulk of health care needs and require a very different approach. There is overwhelming consensus that the prevention and treatment of cardiovascular disease, diabetes, hypertension, chronic pain, obesity, asthma, HIV, and many other chronic illnesses require substantial patient self-management [2].
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paper_title: SociableSense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing

paper_content:

The interactions and social relations among users in workplaces have been studied by many generations of social psychologists. There is evidence that groups of users that interact more in workplaces are more productive. However, it is still hard for social scientists to capture fine-grained data about phenomena of this kind and to find the right means to facilitate interaction. It is also difficult for users to keep track of their level of sociability with colleagues. While mobile phones offer a fantastic platform for harvesting long term and fine grained data, they also pose challenges: battery power is limited and needs to be traded-off for sensor reading accuracy and data transmission, while energy costs in processing computationally intensive tasks are high. In this paper, we propose SociableSense, a smart phones based platform that captures user behavior in office environments, while providing the users with a quantitative measure of their sociability and that of colleagues. We tackle the technical challenges of building such a tool: the system provides an adaptive sampling mechanism as well as models to decide whether to perform computation of tasks, such as the execution of classification and inference algorithms, locally or remotely. We perform several micro-benchmark tests to fine-tune and evaluate the performance of these mechanisms and we show that the adaptive sampling and computation distribution schemes balance trade-offs among accuracy, energy, latency, and data traffic. Finally, by means of a social psychological study with ten participants for two working weeks, we demonstrate that SociableSense fosters interactions among the participants and helps in enhancing their sociability.
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paper_title: Ear-phone: an end-to-end participatory urban noise mapping system

paper_content:

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end participatory urban noise mapping system called Ear-Phone. Ear-Phone, for the first time, leverages Compressive Sensing to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95 and HP iPAQ mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. Extensive simulations and outdoor experiments demonstrate that Ear-Phone is a feasible platform to assess noise pollution, incurring reasonable system resource consumption at mobile devices and providing high reconstruction accuracy of the noise map.
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paper_title: Nericell: rich monitoring of road and traffic conditions using mobile smartphones

paper_content:

We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.
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paper_title: Common Sense: participatory urban sensing using a network of handheld air quality monitors

paper_content:

Poor air quality is a global health issue, causing serious problems like asthma, cancer, and heart disease around the world. Earlier this decade, the World Health Organization estimated that three million people die each year from the effects of air pollution [6]. Unfortunately, while variations in air quality are significant, today's air quality monitors are very sparsely deployed. To address this visibility gap, the Common Sense project is developing participatory sensing systems that allow individuals to measure their personal exposure, groups to aggregate their members' exposure, and activists to mobilize grassroots community action.
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paper_title: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

paper_content:

We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.
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paper_title: HealthAware: Tackling obesity with health aware smart phone systems

paper_content:

Obesity prevention requires individuals to have healthy eating and physical activity awareness in their daily lives. New technological products have been explored to help monitoring individual health behaviors. Smart phones are ubiq­uitous hand-held tools with rich embedded sensors and limited computing power. A smart phone is a prospective candidate to integrate physical exercise analysis and food intake monitoring. This paper presents a novel health-aware smart phone system (HealthAware) which utilizes the embedded accelerometer to monitor daily physical activities and the built-in camera to analyze food items. The system presents the user's physical activity counts at real time to remind how much daily activity is needed to keep healthy. The user takes pictures of food items and the system provides health related information regarding to the food item intakes. The system is composed of an on-device database which holds the user specific data and food item information. HealthAware is a real time practical system to prevent obesity by enhancing individual daily healthy behavior awareness.
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paper_title: Andwellness: An open mobile system for activity and experience sampling

paper_content:

Advances in mobile phone technology have allowed phones to become a convenient platform for real-time assessment of a participants health and behavior. AndWellness, a personal data collection system, uses mobile phones to collect and analyze data from both active, triggered user experience samples and passive logging of onboard environmental sensors. The system includes an application that runs on Android based mobile phones, server software that manages deployments and acts as a central repository for data, and a dashboard front end for both participants and researchers to visualize incoming data in real-time. Our system has gone through testing by researchers in preparation for deployment with participants, and we describe an initial qualitative study plus several planned future studies to demonstrate how our system can be used to better understand a user's health related habits and observations.
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paper_title: User recruitment for mobile crowdsensing over opportunistic networks

paper_content:

We look into the realization of mobile crowdsensing campaigns that draw on the opportunistic networking paradigm, as practised in delay-tolerant networks but also in the emerging device-to-device communication mode in cellular networks. In particular, we ask how mobile users can be optimally selected in order to generate the required space-time paths across the network for collecting data from a set of fixed locations. The users hold different roles in these paths, from collecting data with their sensing-enabled devices to relaying them across the network and uploading them to data collection points with Internet connectivity. We first consider scenarios with deterministic node mobility and formulate the selection of users as a minimum-cost set cover problem with a submodular objective function. We then generalize to more realistic settings with uncertainty about the user mobility. A methodology is devised for translating the statistics of individual user mobility to statistics of spacetime path formation and feeding them to the set cover problem formulation. We describe practical greedy heuristics for the resulting NP-hard problems and compute their approximation ratios. Our experimentation with real mobility datasets (a) illustrates the multiple tradeoffs between the campaign cost and duration, the bound on the hopcount of space-time paths, and the number of collection points; and (b) provides evidence that in realistic problem instances the heuristics perform much better than what their pessimistic worst-case bounds suggest.
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paper_title: Collaborative Data Delivery for Smart City-Oriented Mobile Crowdsensing Systems

paper_content:

The huge increase of population living in cities calls for a sustainable urban development. Mobile crowdsensing (MCS) leverages participation of active citizens to improve performance of existing sensing infrastructures. In typical MCS systems, sensing tasks are allocated and reported on individual-basis. In this paper, we investigate on collaboration among users for data delivery as it brings a number of benefits for both users and sensing campaign organizers and leads to better coordination and use of resources. By taking advantage from proximity, users can employ device-to-device (D2D) communications like Wi-Fi Direct that are more energy efficient than 3G/4G technology. In such scenario, once a group is set, one of its member is elected to be the owner and perform data forwarding to the collector. The efficiency of forming groups and electing suitable owners defines the efficiency of the whole collaborative-based system. This paper proposes three policies optimized for MCS that are compliant with current Android implementation of Wi-Fi Direct. The evaluation results, obtained using CrowdSenSim simulator, demonstrate that collaborative-based approaches outperform significantly individual-based approaches.
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paper_title: How long to wait?: predicting bus arrival time with mobile phone based participatory sensing

paper_content:

The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.
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paper_title: ParkGauge: Gauging the Occupancy of Parking Garages with Crowdsensed Parking Characteristics

paper_content:

Finding available parking spaces in dense urban areas is a globally recognized issue in urban mobility. Whereas prior studies have focused on outdoor/street parking due to a common belief that parking garages are capable of delivering real-time occupancy information, we specifically target at (indoor) parking garages as this belief is far from true. This problem is very challenging as all the infrastructure supports (e.g., GPS and Wi-Fi) assumed by existing proposals are not available to parking garages, so counting how many vehicles are using a parking garage by crowd sensing can be extremely difficult. To this end, we present Park Gauge, a method to gauge the occupancy of parking garages, along with a reference system prototype for performance evaluation, it infers parking occupancy from crowd sensed parking characteristics instead of counting the parked vehicles. Park Gauge adopts low-power sensors (e.g., accelerometer and barometer) in the driver's smartphone to determine the driving states (e.g., turning and braking). A sequence of such states further allows the inference of driving contexts (e.g., driving, queuing and parked) that in turn yield temporal parking characteristics of a parking garage, including time-to-park and time-in-cruising/queuing. Mining such mobile data opportunistically collected from a crowd of drivers arriving at various garages yields a good measure of their occupancies and hence useful recommendations can be generated (in real-time) to inform drivers coming toward these venues. Through extensive experiments, we demonstrate that our method fully explores these parking characteristics to efficiently infer occupancies of parking garages with high accuracy.
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paper_title: Proximity discovery and data dissemination for mobile crowd sensing using LTE direct

paper_content:

Abstract The pervasive and mass-market usage of smartphones and the huge amount of information that those devices with their sensors can detect or produce are enabling novel and unforeseen smart city sensing opportunities through the so-called Mobile Crowd Sensing (MCS) paradigm. MCS aims to involve volunteering citizens in data sensing activities as they roam across the city. Notwithstanding the intense recent research activity in the MCS field, current MCS platforms still exhibit some technological limitations and do not exploit at best socio-technical mechanisms (e.g., economic/social incentives) to facilitate and encourage users’ participation. In this perspective, the recent evolution of direct Device-to-Device (D2D) communications, in particular novel proximity discovery technologies, represents a relevant opportunity for MCS frameworks. This paper proposes a novel solution for improving the MCS impact and citizens’ participation rate by leveraging LTE Direct, i.e., one of the most recent and promising technologies for D2D proximity discovery and local data dissemination in next generation 5G networks. Our original MCS extension supports not only novel interactions with smartphones that do not necessarily host our whole MCS client (only an LTE Direct-enabled sensing module is required), but also enables new Internet of Things (IoT) scenarios where it is possible to extend smartphone sensing capabilities through D2D interactions with sensors that are dynamically determined via LTE Direct proximity discovery. To enable these new MCS features, we propose to expand the communication and data dissemination capabilities of LTE Direct via an original multi-frame transport protocol, capable of working properly also in ultra-dense scenarios with very high frame loss probability.
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paper_title: Real-time task assignment in hyperlocal spatial crowdsourcing under budget constraints

paper_content:

Spatial Crowdsourcing (SC) is a novel platform that engages individuals in the act of collecting various types of spatial data. This method of data collection can significantly reduce cost and turnover time, and is particularly useful in environmental sensing, where traditional means fail to provide fine-grained field data. In this study, we introduce hyperlocal spatial crowdsourcing, where all workers who are located within the spatiotemporal vicinity of a task are eligible to perform the task, e.g., reporting the precipitation level at their area and time. In this setting, there is often a budget constraint, either for every time period or for the entire campaign, on the number of workers to activate to perform tasks. The challenge is thus to maximize the number of assigned tasks under the budget constraint, despite the dynamic arrivals of workers and tasks as well as their co-location relationship. We study two problem variants in this paper: budget is constrained for every timestamp, i.e. fixed, and budget is constrained for the entire campaign, i.e. dynamic. For each variant, we study the complexity of its offline version and then propose several heuristics for the online version which exploit the spatial and temporal knowledge acquired over time. Extensive experiments with real-world and synthetic datasets show the effectiveness and efficiency of our proposed solutions.
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paper_title: Bluetooth based collaborative crowd density estimation with mobile phones

paper_content:

We present a technique for estimating crowd density by using a mobile phone to scan the environment for Bluetooth devices. The paper builds on previous work directed to use Bluetooth scans to analyze social context and extends it with more advanced features, leveraging collaboration between close by devices, and the use of relative features that do not directly depend on the absolute number of devices in the environment. The method is evaluated on a data set from an experiment at the public viewing event in Kaiserslautern during the European soccer championship showing over 75% recognition accuracy on seven discrete classes.
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paper_title: LiveCompare: grocery bargain hunting through participatory sensing

paper_content:

Many consumers are misled into paying high prices due to the search costs associated with attaining price information [16]. The popularity of bargain-hunting web sites like Slickdeals.net, which boasts 2.5 million visitors per month, hints that many shoppers are indeed in search of tools to help them save money. We present LiveCompare, a system that leverages the ubiquity of mobile camera phones to allow for grocery bargain hunting through participatory sensing. We utilize two-dimensional barcode decoding to automatically identify grocery products, as well as localization techniques to automatically pinpoint store locations. We show that an incentive scheme is inherently ingrained into our query/response protocol, and we suggest self-regulating mechanisms for preserving data integrity. As a result, we demonstrate that money-saving price comparisons can be conducted among brick and mortar grocery stores without the explicit cooperation of the stores.
---
paper_title: Noisemap - real-time participatory noise maps

paper_content:

Noise pollution is a problem increasingly acknowledged ::: by authorities and governments around the globe. However, ::: creating noise maps with conventional methods is either inaccurate ::: or very expensive. To increase the spatial and temporal ::: data resolution a high number of sensors must be deployed. ::: In this paper we present results and prototypes based ::: on participatory sensing leading to accurate, real-time noise ::: maps. First we present NoiseMap a application currently ::: released for Android phones. NoiseMap gathers data on ::: loudness and transfers it to the open urban sensing platform ::: da sense. da sense allows users to access and control their ::: data, generate real-time noise maps and data graphs. Public ::: data is made available using either a web service or a ::: JavaScript API.
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paper_title: Participatory Air Pollution Monitoring Using Smartphones

paper_content:

Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on o-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the eect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.
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paper_title: Nericell: rich monitoring of road and traffic conditions using mobile smartphones

paper_content:

We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.
---
paper_title: Bluetooth based collaborative crowd density estimation with mobile phones

paper_content:

We present a technique for estimating crowd density by using a mobile phone to scan the environment for Bluetooth devices. The paper builds on previous work directed to use Bluetooth scans to analyze social context and extends it with more advanced features, leveraging collaboration between close by devices, and the use of relative features that do not directly depend on the absolute number of devices in the environment. The method is evaluated on a data set from an experiment at the public viewing event in Kaiserslautern during the European soccer championship showing over 75% recognition accuracy on seven discrete classes.
---
paper_title: TaskMe: multi-task allocation in mobile crowd sensing

paper_content:

Task allocation or participant selection is a key issue in Mobile Crowd Sensing (MCS). While previous participant selection approaches mainly focus on selecting a proper subset of users for a single MCS task, multi-task-oriented participant selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes TaskMe, a participant selection framework for multi-task MCS environments. In particular, two typical multi-task allocation situations with bi-objective optimization goals are studied: (1) For FPMT (few participants, more tasks), each participant is required to complete multiple tasks and the optimization goal is to maximize the total number of accomplished tasks while minimizing the total movement distance. (2) For MPFT (more participants, few tasks), each participant is selected to perform one task based on pre-registered working areas in view of privacy, and the optimization objective is to minimize total incentive payments while minimizing the total traveling distance. Two optimal algorithms based on the Minimum Cost Maximum Flow theory are proposed for FPMT, and two algorithms based on the multi-objective optimization theory are proposed for MPFT. Experiments verify that the proposed algorithms outperform baselines based on a large-scale real-word dataset under different experiment settings (the number of tasks, various task distributions, etc.).
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paper_title: Efficient use of solar chargers with the help of ambient light sensors on smartphones

paper_content:

This paper discusses the possibilities to measure the amount of light with the help of portable devices such as mobile phones and tablets. Focus is directed to the accuracy of the ambient light sensor on smartphones in order to obtain the illuminance indoors and the solar radiation level outdoors. In general, information on the ambient conditions is vital to improve the performance of solar chargers. For example, if users are able to allocate beneficial locations to deploy solar chargers inside buildings, up to 100 times more energy can be gathered during the same periodic time. Similarly, under outdoor environmental conditions, solar modules can be aligned better towards the sun to increase the possible amount of output power. We analyse the accuracy of ambient light sensors which are available in today’s low-cost and upper-class smartphones. Additionally, we present calibration strategies for ambient light sensors in order to minimise the error between conventional measurement equipment and mobile phones.
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paper_title: Energy-efficient collaborative sensing with mobile phones

paper_content:

Mobile phones with a rich set of embedded sensors enable sensing applications in various domains. In this paper, we propose to leverage cloud-assisted collaborative sensing to reduce sensing energy consumption for mobile phone sensing applications. We formally define a minimum energy sensing scheduling problem and present a polynomial-time algorithm to obtain optimal solutions, which can be used to show energy savings that can potentially be achieved by using collaborative sensing in mobile phone sensing applications, and can also serve as a benchmark for performance evaluation. We also address individual energy consumption and fairness by presenting an algorithm to find fair energy-efficient sensing schedules. Under realistic assumptions, we present two practical and effective heuristic algorithms to find energy-efficient sensing schedules. It has been shown by simulation results based on real energy consumption (measured by the Monsoon power monitor) and location (collected from the Google Map) data that collaborative sensing significantly reduces energy consumption compared to a traditional approach without collaborations, and the proposed heuristic algorithm performs well in terms of both total energy consumption and fairness.
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paper_title: NoiseTube: Measuring and mapping noise pollution with mobile phones

paper_content:

In this paper we present a new approach for the assessment of noise pollution involving the general public. The goal of this project is to turn GPS-equipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment. Thus each user can contribute by sharing their geo-localised measurements and further personal annotation to produce a collective noise map.
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paper_title: Activity Recognition from Accelerometer Data

paper_content:

Activity recognition fits within the bigger framework of context awareness. In this paper, we report on our efforts to recognize user activity from accelerometer data. Activity recognition is formulated as a classification problem. Performance of base-level classifiers and meta-level classifiers is compared. Plurality Voting is found to perform consistently well across different settings.
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paper_title: LiveCompare: grocery bargain hunting through participatory sensing

paper_content:

Many consumers are misled into paying high prices due to the search costs associated with attaining price information [16]. The popularity of bargain-hunting web sites like Slickdeals.net, which boasts 2.5 million visitors per month, hints that many shoppers are indeed in search of tools to help them save money. We present LiveCompare, a system that leverages the ubiquity of mobile camera phones to allow for grocery bargain hunting through participatory sensing. We utilize two-dimensional barcode decoding to automatically identify grocery products, as well as localization techniques to automatically pinpoint store locations. We show that an incentive scheme is inherently ingrained into our query/response protocol, and we suggest self-regulating mechanisms for preserving data integrity. As a result, we demonstrate that money-saving price comparisons can be conducted among brick and mortar grocery stores without the explicit cooperation of the stores.
---
paper_title: Why energy matters? Profiling energy consumption of mobile crowdsensing data collection frameworks

paper_content:

Abstract Mobile Crowdsensing (MCS) has emerged in the last years and has become one of the most prominent paradigms for urban sensing. The citizens actively participate in the sensing process by contributing data with their mobile devices. To produce data, citizens sustain costs, i.e., the energy consumed for sensing and reporting operations. Hence, devising energy efficient data collection frameworks (DCFs) is essential to foster participation. In this work, we investigate from an energy-perspective the performance of different DCFs. Our methodology is as follows: (i) we developed an Android application that implements the DCFs, (ii) we profiled the energy and network performance with a power monitor and Wireshark, (iii) we included the obtained traces into CrowdSenSim simulator for large-scale evaluations in city-wide scenarios such as Luxembourg City, Turin and Washington DC. The amount of collected data, energy consumption and fairness are the performance indexes evaluated. The results unveil that DCFs with continuous data reporting require particular adjustments to be more energy-effective in harvesting data from the crowd than DCFs with probabilistic reporting. The latter exhibit high variability of energy consumption, i.e., to produce the same amount of data, the associated energy cost of different users can vary significantly.
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paper_title: Opportunities in mobile crowd sensing

paper_content:

Mobile crowd sensing is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous largescale applications. Human involvement is one of the most important features, and human mobility offers unprecedented opportunities for both sensing coverage and data transmission. In this article, we investigate the opportunistic characteristics of human mobility from the perspectives of both sensing and transmission, and discuss how to exploit these opportunities to collect data efficiently and effectively. We also outline various open issues brought by human involvement in this emerging research area.
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paper_title: How long to wait?: predicting bus arrival time with mobile phone based participatory sensing

paper_content:

The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.
---
paper_title: Efficient and flexible crowdsourcing of specialized tasks with precedence constraints

paper_content:

Many companies now use crowdsourcing to leverage external (as well as internal) crowds to perform specialized work, and so methods of improving efficiency are critical. Tasks in crowdsourcing systems with specialized work have multiple steps and each step requires multiple skills. Steps may have different flexibilities in terms of obtaining service from one or multiple agents, due to varying levels of dependency among parts of steps. Steps of a task may have precedence constraints among them. Moreover, there are variations in loads of different types of tasks requiring different skill-sets and availabilities of different types of agents with different skill-sets. Considering these constraints together necessitates the design of novel schemes to allocate steps to agents. In addition, large crowdsourcing systems require allocation schemes that are simple, fast, decentralized and offer customers (task requesters) the freedom to choose agents. In this work we study the performance limits of such crowdsourcing systems and propose efficient allocation schemes that provably meet the performance limits under these additional requirements. We demonstrate our algorithms on data from a crowdsourcing platform run by a non-profit company and show significant improvements over current practice.
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paper_title: Bootstrap Based Uncertainty Propagation for Data Quality Estimation in Crowdsensing Systems

paper_content:

The diffusion of mobile devices equipped with sensing, computation, and communication capabilities is opening unprecedented possibilities for high-resolution, spatio-temporal mapping of several phenomena. This novel data generation, collection, and processing paradigm, termed crowdsensing, lays upon complex, distributed cyberphysical systems. Collective data gathering from heterogeneous, spatially distributed devices inherently raises the question of how to manage different quality levels of contributed data. In order to extract meaningful information, it is, therefore, desirable to the introduction of effective methods for evaluating the quality of data. In this paper, we propose an approach aimed at systematic accuracy estimation of quantities provided by end-user devices of a crowd-based sensing system. This is obtained thanks to the combination of statistical bootstrap with uncertainty propagation techniques, leading to a consistent and technically sound methodology. Uncertainty propagation provides a formal framework for combining uncertainties, resulting from different quantities influencing a given measurement activity. Statistical bootstrap enables the characterization of the sampling distribution of a given statistics without any prior assumption on the type of statistical distributions behind the data generation process. The proposed approach is evaluated on synthetic benchmarks and on a real world case study. Cross-validation experiments show that confidence intervals computed by means of the presented technique show a maximum 1.5% variation with respect to interval widths computed by means of controlled standard Monte Carlo methods, under a wide range of operating conditions. In general, experimental results confirm the suitability and validity of the introduced methodology.
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paper_title: Activity recognition using smartphone sensors

paper_content:

Motion sensor embedded smartphones have provided a new platform for activity inference. These sensors, initially used for cell phone feature enhancement, are now being used for a variety of applications. Providing cell phone users information about their own physical activity in an understandable format can enable users to make more informed and healthier lifestyle choices. In this work, we built a smartphone application which tracks users' physical activities and provide feedback requiring no user input during routine operation. The application reports estimates of the calories burned, broken up by physical activities. Detectable physical activities include walking, running, climbing stairs, descending stairs, driving, cycling and being inactive. We evaluated a number of classification algorithms from the area of Machine Learning, including Naive Bayes, Decision Tree, K-Nearest Neighbor and Support Vector Machine classifiers. For training and verification of classifiers, we collected a dataset of 510 activity traces using cell phone sensors. We developed a smartphone app that performs activity recognition that does not require any user intervention. The classifier implemented in the Android app performs at an average true positives rate of greater than 95%, false positives rate of less than 1.5% and an ROC area of greater than 98%.
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paper_title: From participatory sensing to Mobile Crowd Sensing

paper_content:

The research on the efforts of combining human and machine intelligence has a long history. With the development of mobile sensing and mobile Internet techniques, a new sensing paradigm called Mobile Crowd Sensing (MCS), which leverages the power of citizens for large-scale sensing has become popular in recent years. As an evolution of participatory sensing, MCS has two unique features: (1) it involves both implicit and explicit participation; (2) MCS collects data from two user-participant data sources: mobile social networks and mobile sensing. This paper presents the literary history of MCS and its unique issues. A reference framework for MCS systems is also proposed. We further clarify the potential fusion of human and machine intelligence in MCS. Finally, we discuss the future research trends as well as our efforts to MCS.
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paper_title: Towards Physical Activity Recognition Using Smartphone Sensors

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In recent years, the use of a smartphone accelerometer in physical activity recognition has been well studied. However, the role of a gyroscope and a magnetometer is yet to be explored, both when used alone as well as in combination with an accelerometer. For this purpose, we investigate the role of these three smartphone sensors in activity recognition. We evaluate their roles on four body positions using seven classifiers while recognizing six physical activities. We show that in general an accelerometer and a gyroscope complement each other, thereby making the recognition process more reliable. Moreover, in most cases, a gyroscope does not only improve the recognition accuracy in combination with an accelerometer, but it also achieves a reasonable performance when used alone. The results for a magnetometer are not encouraging because it causes over-fitting in training classifiers due to its dependence on directions. Based on our evaluations, we show that it is difficult to make an exact general statement about which sensor performs better than the others in all situations because their recognition performance depends on the smartphone's position, the selected classifier, and the activity being recognized. However, statements about their roles in specific situations can be made. We report our observations and results in detail in this paper, while our data-set and data-collection app is publicly available, thereby making our experiments reproducible.
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paper_title: Participatory Air Pollution Monitoring Using Smartphones

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Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on o-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the eect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.
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paper_title: Mobile Phone Sensing Systems: A Survey

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Mobile phone sensing is an emerging area of interest for researchers as smart phones are becoming the core communication device in people's everyday lives. Sensor enabled mobile phones or smart phones are hovering to be at the center of a next revolution in social networks, green applications, global environmental monitoring, personal and community healthcare, sensor augmented gaming, virtual reality and smart transportation systems. More and more organizations and people are discovering how mobile phones can be used for social impact, including how to use mobile technology for environmental protection, sensing, and to leverage just-in-time information to make our movements and actions more environmentally friendly. In this paper we have described comprehensively all those systems which are using smart phones and mobile phone sensors for humans good will and better human phone interaction.
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paper_title: Mobile Crowd Sensing and Computing: The Review of an Emerging Human-Powered Sensing Paradigm

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With the surging of smartphone sensing, wireless networking, and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC) has become a promising paradigm for cross-space and large-scale sensing. MCSC extends the vision of participatory sensing by leveraging both participatory sensory data from mobile devices (offline) and user-contributed data from mobile social networking services (online). Further, it explores the complementary roles and presents the fusion/collaboration of machine and human intelligence in the crowd sensing and computing processes. This article characterizes the unique features and novel application areas of MCSC and proposes a reference framework for building human-in-the-loop MCSC systems. We further clarify the complementary nature of human and machine intelligence and envision the potential of deep-fused human--machine systems. We conclude by discussing the limitations, open issues, and research opportunities of MCSC.
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paper_title: Automatic Collection of Fuel Prices from a Network of Mobile Cameras

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It is an undeniable fact that people want information. Unfortunately, even in today's highly automated society, a lot of the information we desire is still manually collected. An example is fuel prices where websites providing fuel price information either send their workers out to manually collect the prices or depend on volunteers manually relaying the information. This paper proposes a novel application of wireless sensor networks to automatically collect fuel prices from camera images of road-side price board (billboard) of service (or gas) stations. Our system exploits the ubiquity of mobile phones that have cameras as well as users contributing and sharing data. In our proposed system, cameras of contributing users will be automatically triggered when they get close to a service station. These images will then be processed by computer vision algorithms to extract the fuel prices. In this paper, we will describe the system architecture and present results from our computer vision algorithms. Based on 52 images, our system achieves a hit rate of 92.3% for correctly detecting the fuel price board from the image background and reads the prices correctly in 87.7% of them. To the best of our knowledge, this is the first instance of a sensor network being used for collecting consumer pricing information.
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paper_title: User activity recognition method based on atmospheric pressure sensing

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Several studies have been conducted on context recognition as well as hobby and preference extraction by analyzing the data obtained from the sensors in a smartphone. As a smartphone component, a barometer is expected to be useful for activity recognition because of its low power consumption. In this work, we propose an activity recognition method of classifying a user's state into indoor and outdoor states and using a barometer at each state. In the proposed method, the floor of a building on which a user is located is estimated by determining atmospheric pressure variations sensed in the indoor state, and the user's location is estimated by determining atmospheric pressure variations according to the user movement along a track in the outdoor state. In particular, this paper delineates the method of estimating the current floor on which the user is located. We confirmed that it is possible to closely estimate the current floor of the building in the case of user movement among eighteen floors.
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paper_title: Movement pattern recognition through smartphone's accelerometer

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Sensor-enabled smartphone's have become a mainstream platform for researchers due to their ability to collect and process large quantities of data, hence creating new opportunities for innovative applications. Yet, the limits in employing sensors to opportunistically detect human behaviors are not clear and deserve investigation. To this purpose, in this article, we discuss movement pattern recognition in day-by-day urban street behavior. As a case study, we restrict at recognizing situations when a pedestrian stops, crosses a street ruled by a traffic light; to do so we only use data coming from the accelerometer of the pedestrian's smartphone.
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paper_title: Driving style recognition using a smartphone as a sensor platform

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Driving style can characteristically be divided into two categories: “typical” (non-aggressive) and aggressive. Understanding and recognizing driving events that fall into these categories can aid in vehicle safety systems. Potentially-aggressive driving behavior is currently a leading cause of traffic fatalities in the United States. More often than not, drivers are unaware that they commit potentially-aggressive actions daily. To increase awareness and promote driver safety, we are proposing a novel system that uses Dynamic Time Warping (DTW) and smartphone based sensor-fusion (accelerometer, gyroscope, magnetometer, GPS, video) to detect, recognize and record these actions without external processing. Our system differs from past driving pattern recognition research by fusing related inter-axial data from multiple sensors into a single classifier. It also utilizes Euler representation of device attitude (also based on fused data) to aid in classification. All processing is done completely on the smartphone.
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paper_title: Incentive Mechanisms for Mobile Crowd Sensing: Current States and Challenges of Work

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Mobile crowd sensing (MCS) is a new paradigm which leverages the ubiquity of sensor-equipped mobile devices such as smartphones, music players, and in-vehicle sensors at the edge of the Internet, to collect data. The new paradigm will fuel the evolution of the Internet of Things to three changes as follows: First, the terminal devices at the edge of the Internet change from PCs to mobile phones. Second, the interactive mode extends from the virtual space to the real physical world. Thirdly, the forwarding manner of sensing data are undergoing the transition from the priori to the opportunistic. To better meet the demands of MCS applications at a societal scale, incentive mechanisms are indispensable. In this paper, we will first overview three categories of MCS applications, and then propose a new architecture for MCS applications. Based on the architecture, we discuss various research challenges about incentive mechanism designs for MCS applications, followed by potential future work discussions. Finally, we present potential future works.
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paper_title: Movement detection for power-efficient smartphone WLAN localization

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Mobile phone services based on the location of a user have increased in popularity and importance, particularly with the proliferation of feature-rich smartphones. One major obstacle to the widespread use of location-based services is the limited battery life of these mobile devices and the high power costs of many existing approaches. We demonstrate the effectiveness of a localization strategy that performs full localization only when it detects a user has finished moving. We characterize the power use of a smartphone, then verify our strategy using models of long-term walk behavior, recorded data, and device implementation. For the same sample period, our movement-informed strategy reduces power consumption compared to existing approaches by more than 80% with an impact on accuracy of less than 5%. This difference can help achieve the goal of near-continuous localization on mobile devices.
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paper_title: Retrofitting smartphones to be used as particulate matter dosimeters

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This work discusses ways of measuring particulate matter with mobile devices. Solutions using a dedicated sensor device are presented along with a novel method of retrofitting a sensor to a camera phone without need for electrical modifications. Instead, the flash and camera of the phone are used as light source and receptor of an optical dust sensor respectively. Experiments to evaluate the accuracy are presented.
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paper_title: Remote sensing platforms and sensors: A survey

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Abstract The objective of this article is to review the state-of-the-art remote sensing technologies, including platforms and sensors, the topics representing the primary research interest in the ISPRS Technical Commission I activities. Due to ever advancing technologies, the remote sensing field is experiencing unprecedented developments recently, fueled by sensor advancements and continuously increasing information infrastructure. The scope and performance potential of sensors in terms of spatial, spectral and temporal sensing abilities have expanded far beyond the traditional boundaries of remote sensing, resulting in significantly better observation capabilities. First, platform developments are reviewed with the main focus on emerging new remote sensing satellite constellations and UAS (Unmanned Aerial System) platforms. Next, sensor georeferencing and supporting navigation infrastructure, an enabling technology for remote sensing, are discussed. Finally, we group sensors based on their spatial, spectral and temporal characteristics, and classify them by their platform deployment competencies. In addition, we identify current trends, including the convergence between the remote sensing and navigation field, and the emergence of cooperative sensing, and the potential of crowdsensing.
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paper_title: Demystifying energy usage in smartphones

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In this paper, we presented our recent characterization and analysis on the power consumption of smartphone radio components, including Wi-Fi, GPS and cellular (3G/4G) modules. Different from previous research that focused on the properties of single module under a limited number of usage scenarios, our works are performed on the statistically selected representative Apps with four generations of Samsung Galaxy series smartphones. We found that over the four characterized generations, the average power consumption of the smartphones with a connected Wi-Fi network on the selected Apps increases about 38.20%, indicating more and more energy-hungry designs. However, the raising of the power consumption is not mainly from the radio modules as the power efficiency of the Wi-Fi module indeed improves by about 21%. The 4G module consumes about 15.13% more power compared to the latest 3G module. But the power efficiency of 3G module improves by averagely 15.17% over the four generations. The GPS module also shows 25.80% power consumption improvement over the generations. We also show that the characterized power results can be combined with the real user traces for detailed daily energy usage estimation. Our research established an ethological and evolutionary power consumption trend of smartphones, which will guide the power optimization in the next-generation smartphone design.
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paper_title: Development of Mobile Radiation Monitoring System Utilizing Smartphone and Its Field Tests in Fukushima

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We developed a series of inexpensive but accurate and mobile radiation detectors, which we named Pocket Geiger (POKEGA), to address the desire of ordinary people to own a radiation detector following the March 2011 Daiichi Nuclear Power Plant accidents in Fukushima, Japan. To reduce costs while maintaining accuracy and flexibility, we used a combination of a p-i-n photodiode detector connected to a smartphone via a microphone cable. The detector circuit design is optimized for simplicity and low cost, whereas the smartphone software application is tasked with handling the complex processing required. Furthermore, the device also used the GPS and networking capabilities of the smartphone for logging and data sharing. The 137Cs measurement range for a POKEGA-equipped smartphone is approximately from 0.05 to 10 mSv/h, which covers most radiation levels measured in Japan. Approximately 12000 POKEGA units were shipped in the six months following its release, and 2000 users have joined a Facebook community where they report measurement results and discuss hardware and software improvements. In parallel, we have addressed practical problems for POKEGA, such as vibration noise, energy consumption, and operating temperature, by conducting field tests in the Fukushima evacuation zone. The POKEGA series has been improved by solving such issues. This article reports on a new style of pragmatic sensor networking methodology, from the aspects of emergency response engineering, open-sourced development, and consumer-generated measurements.
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paper_title: Recognizing Human Activities User-independently on Smartphones Based on Accelerometer Data

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Real-time human activity recognition on a mobile phone is presented in this article. Unlike in most other studies, not only the data were collected using the accelerometers of a smartphone, but also models were implemented to the phone and the whole classification process (preprocessing, feature extraction and classification) was done on the device. The system is trained using phone orientation independent features to recognize five everyday activities: walking, running, cycling, driving a car and sitting/standing while the phone is in the pocket of the subject's trousers. Two classifiers were compared, knn (k nearest neighbors) and QDA (quadratic discriminant analysis). The models for real-time activity recognition were trained offline using a data set collected from eight subjects and these offline results were compared to real-time recognition rates, which are obtained by implementing models to mobile activity recognition application which currently supports two operating systems: Symbian^3 and Android. The results show that the presented method is light and, therefore, suitable for be used in real-time recognition. In addition, the recognition rates on the smartphones were encouraging, in fact, the recognition accuracies obtained are approximately as high as offline recognition rates. Also, the results show that the method presented is not an operating system dependent.
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paper_title: Common Sense: participatory urban sensing using a network of handheld air quality monitors

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Poor air quality is a global health issue, causing serious problems like asthma, cancer, and heart disease around the world. Earlier this decade, the World Health Organization estimated that three million people die each year from the effects of air pollution [6]. Unfortunately, while variations in air quality are significant, today's air quality monitors are very sparsely deployed. To address this visibility gap, the Common Sense project is developing participatory sensing systems that allow individuals to measure their personal exposure, groups to aggregate their members' exposure, and activists to mobilize grassroots community action.
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paper_title: Large-Scale Distributed Dedicated- and Non-Dedicated Smart City Sensing Systems

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The past decade has witnessed an explosion of interest in smart cities in which a set of applications, such as smart healthcare, smart lighting, and smart transportation promise to drastically improve the quality and efficiency of these services. The skeleton of these applications is formed by a network of distributed sensors that captures data, pre-processes, and transmits it to a center for further processing. While these sensors are generally perceived to be a wireless network of sensing devices that are deployed permanently as part of an application, the emerging mobile crowd-sensing (MCS) concept prescribes a drastically different platform for sensing; a network of smartphones, owned by a volunteer crowd, can capture, pre-process, and transmit the data to the same center. We call these two forms of sensors dedicated and non-dedicated sensors in this paper. While dedicated sensors imply higher deployment and maintenance costs, the MCS concept also has known implementation challenges, such as incentivizing the crowd and ensuring the trustworthiness of the captured data, and covering a wide sensing area. Due to the pros/cons of each option, the decision as to which one is better becomes a non-trivial answer. In this paper, we conduct a thorough study of both types of sensors and draw conclusions about which one becomes a favourable option based on a given application platform.
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paper_title: Multi-task assignment for crowdsensing in mobile social networks

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Mobile crowdsensing is a new paradigm in which a crowd of mobile users exploit their carried smart devices to conduct complex computation and sensing tasks in mobile social networks (MSNs). In this paper, we focus on the task assignment problem in mobile crowdsensing. Unlike traditional task scheduling problems, the task assignment in mobile crowdsensing must follow the mobility model of users in MSNs. To solve this problem, we propose an oFfline Task Assignment (FTA) algorithm and an oNline Task Assignment (NTA) algorithm. Both FTA and NTA adopt a greedy task assignment strategy. Moreover, we prove that the FTA algorithm is an optimal offline task assignment algorithm, and give a competitive ratio of the NTA algorithm. In addition, we demonstrate the significant performance of our algorithms through extensive simulations, based on four real MSN traces and a synthetic MSN trace.
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paper_title: NoiseTube: Measuring and mapping noise pollution with mobile phones

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In this paper we present a new approach for the assessment of noise pollution involving the general public. The goal of this project is to turn GPS-equipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment. Thus each user can contribute by sharing their geo-localised measurements and further personal annotation to produce a collective noise map.
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paper_title: Urban WiFi characterization via mobile crowdsensing

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We present a mobile crowdsensing approach for urban WiFi characterization that leverages commodity smartphones and the natural mobility of people. Specifically, we report measurement results obtained for Edinburgh, a representative European city, on detecting the presence of deployedWiFi APs via the mobile crowdsensing approach. They show that few channels in 2.4GHz are heavily used; in contrast, there is hardly any activity in the 5GHz band even though relatively it has a greater number of available channels. Spatial analysis of spectrum usage reveals that mutual interference among nearby APs operating in the same channel can be a serious problem with around 10 APs contending with each other in many locations. We find that the characteristics of WiFi deployments at city-scale are similar to that of WiFi deployments in public spaces of different indoor environments. We validate our approach in comparison with wardriving, and also show that our findings generally match with previous studies based on other measurement approaches. As an application of the mobile crowdsensing based urban WiFi monitoring, we outline a cloud based WiFi router configuration service for better interference management with global awareness in urban areas.
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paper_title: Improving location reliability in crowd sensed data with minimal efforts

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People-centric sensing with smart phones can be used for large scale sensing of the physical world by leveraging the cameras, microphones, GPSs, accelerometers, and other sensors on the phones. Ranging from manual photo tasks to automated sensing tasks for activity monitoring, any task can be crowd sourced to smart phones to sense data from different locations at reduced cost. However, the sensed data submitted by participants is not always reliable as they can submit false data to earn money without executing the actual task. Therefore, it is important to validate the sensed data. Validating the context of every sensed data point of each participant is not a scalable solution. One alternative is to first validate the location associated with the sensed data points in order to achieve a certain degree of reliability about the sensed data. However, location validation without support from the wireless carriers is difficult. To address this problem, we propose ILR, a scheme in which we Improve the Location Reliability of mobile crowd sensed data with minimal human efforts. In this scheme, we bootstrap the trust in the system by first manually or automatically using image processing techniques validating a small number of photos submitted by participants. Based on these validations, the location of these photos is assumed to be trusted. Second, we extend this location trust to co-located sensed data points found in the Bluetooth range of the devices that provided the validated photos. This transitive trust is extended until all the co-located tasks are trusted or no new data points are found. In addition, the scheme also helps to detect false location claims associated with sensed data. We applied ILR on data collected from our McSense prototype deployed on Android phones used by students on our campus and detected a significant percentage of the malicious users. Simulation results demonstrate that ILR works well at various densities and helps detect the false location claims based on a minimal numher of validations.
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paper_title: Micro-Blog: sharing and querying content through mobile phones and social participation

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Recent years have witnessed the impacts of distributed content sharing (Wikipedia, Blogger), social networks (Facebook, MySpace), sensor networks, and pervasive computing. We believe that significant more impact is latent in the convergence of these ideas on the mobile phone platform. Phones can be envisioned as people-centric sensors capable of aggregating participatory as well as sensory inputs from local surroundings. The inputs can be visualized in different dimensions, such as space and time. When plugged into the Internet, the collaborative inputs from phones may enable a high resolution view of the world. This paper presents the architecture and implementation of one such system, called Micro-Blog. New kinds of application-driven challenges are identified and addressed in the context of this system. Implemented on Nokia N95 mobile phones, Micro-Blog was distributed to volunteers for real life use. Promising feedback suggests that Micro-Blog can be a deployable tool for sharing, browsing, and querying global information.
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paper_title: LiveCompare: grocery bargain hunting through participatory sensing

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Many consumers are misled into paying high prices due to the search costs associated with attaining price information [16]. The popularity of bargain-hunting web sites like Slickdeals.net, which boasts 2.5 million visitors per month, hints that many shoppers are indeed in search of tools to help them save money. We present LiveCompare, a system that leverages the ubiquity of mobile camera phones to allow for grocery bargain hunting through participatory sensing. We utilize two-dimensional barcode decoding to automatically identify grocery products, as well as localization techniques to automatically pinpoint store locations. We show that an incentive scheme is inherently ingrained into our query/response protocol, and we suggest self-regulating mechanisms for preserving data integrity. As a result, we demonstrate that money-saving price comparisons can be conducted among brick and mortar grocery stores without the explicit cooperation of the stores.
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paper_title: Social serendipity: mobilizing social software

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Many mobile devices incorporate low-power wireless connectivity protocols, such as Bluetooth, that can be used to identify an individual to other people nearby. We have developed an architecture that leverages this functionality in mobile phones - originally designed for communication at a distance - to connect people across the room. Serendipity is an application of the architecture. It combines the existing communications infrastructure with online introduction systems' functionality to facilitate interactions between physically proximate people through a centralized server. A new mobile-phone-based system uses Bluetooth hardware addresses and a database of user profiles to cue informal, face-to-face interactions between nearby users who don't know each other, but probably should.
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paper_title: Darwin phones: the evolution of sensing and inference on mobile phones

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We present Darwin, an enabling technology for mobile phone sensing that combines collaborative sensing and classification techniques to reason about human behavior and context on mobile phones. Darwin advances mobile phone sensing through the deployment of efficient but sophisticated machine learning techniques specifically designed to run directly on sensor-enabled mobile phones (i.e., smartphones). Darwin tackles three key sensing and inference challenges that are barriers to mass-scale adoption of mobile phone sensing applications: (i) the human-burden of training classifiers, (ii) the ability to perform reliably in different environments (e.g., indoor, outdoor) and (iii) the ability to scale to a large number of phones without jeopardizing the "phone experience" (e.g., usability and battery lifetime). Darwin is a collaborative reasoning framework built on three concepts: classifier/model evolution, model pooling, and collaborative inference. To the best of our knowledge Darwin is the first system that applies distributed machine learning techniques and collaborative inference concepts to mobile phones. We implement the Darwin system on the Nokia N97 and Apple iPhone. While Darwin represents a general framework applicable to a wide variety of emerging mobile sensing applications, we implement a speaker recognition application and an augmented reality application to evaluate the benefits of Darwin. We show experimental results from eight individuals carrying Nokia N97s and demonstrate that Darwin improves the reliability and scalability of the proof-of-concept speaker recognition application without additional burden to users.
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paper_title: How long to wait?: predicting bus arrival time with mobile phone based participatory sensing

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The bus arrival time is primary information to most city transport travelers. Excessively long waiting time at bus stops often discourages the travelers and makes them reluctant to take buses. In this paper, we present a bus arrival time prediction system based on bus passengers' participatory sensing. With commodity mobile phones, the bus passengers' surrounding environmental context is effectively collected and utilized to estimate the bus traveling routes and predict bus arrival time at various bus stops. The proposed system solely relies on the collaborative effort of the participating users and is independent from the bus operating companies, so it can be easily adopted to support universal bus service systems without requesting support from particular bus operating companies. Instead of referring to GPS enabled location information, we resolve to more generally available and energy efficient sensing resources, including cell tower signals, movement statuses, audio recordings, etc., which bring less burden to the participatory party and encourage their participation. We develop a prototype system with different types of Android based mobile phones and comprehensively experiment over a 7 week period. The evaluation results suggest that the proposed system achieves outstanding prediction accuracy compared with those bus company initiated and GPS supported solutions. At the same time, the proposed solution is more generally available and energy friendly.
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paper_title: Noisemap - real-time participatory noise maps

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Noise pollution is a problem increasingly acknowledged ::: by authorities and governments around the globe. However, ::: creating noise maps with conventional methods is either inaccurate ::: or very expensive. To increase the spatial and temporal ::: data resolution a high number of sensors must be deployed. ::: In this paper we present results and prototypes based ::: on participatory sensing leading to accurate, real-time noise ::: maps. First we present NoiseMap a application currently ::: released for Android phones. NoiseMap gathers data on ::: loudness and transfers it to the open urban sensing platform ::: da sense. da sense allows users to access and control their ::: data, generate real-time noise maps and data graphs. Public ::: data is made available using either a web service or a ::: JavaScript API.
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paper_title: PEIR, the personal environmental impact report, as a platform for participatory sensing systems research

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PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.
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paper_title: Image browsing, processing, and clustering for participatory sensing: lessons from a DietSense prototype

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Imagers are an increasingly significant source of sensory observations about human activity and the urban environment. ImageScape is a software tool for processing, clustering, and browsing large sets of images. Implemented as a set of web services with an Adobe Flash-based user interface, it supports clustering by both image features and context tags, as well as re-tagging of images in the user interface. Though expected to be useful in many applications, ImageScape was designed as an analysis component of DietSense, a software system under development at UCLA to support (1) the use of mobile devices for automatic multimedia documentation of dietary choices with just-in-time annotation, (2) efficient post facto review of captured media by participants and researchers, and (3) easy authoring and dissemination of the automatic data collection protocols. A pilot study, in which participants ran software that enabled their phones to autonomously capture images of their plates during mealtime, was conducted using an early prototype of the DietSense system, and the resulting image set used in the creation of ImageScape. ImageScape will support two kinds of users within the DietSense application: The participants in dietary studies will have the ability to easily audit their images, while the recipients of the images, health care professionals managing studies and performing analysis, will be able to rapidly browse and annotate large sets of images.
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paper_title: WreckWatch : Automatic Traffic Accident Detection and Notification with Smartphones

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Traffic accidents are one of the leading causes of fatalities in the US. An important indicator of survival rates after an accident is the time between the accident and when emergency medical personnel are dispatched to the scene. Eliminating the time between when an accident occurs and when first responders are dispatched to the scene decreases mortality rates by 6%. One approach to eliminating the delay between accident occurrence and first responder dispatch is to use in-vehicle automatic accident detection and notification systems, which sense when traffic accidents occur and immediately notify emergency personnel. These in-vehicle systems, however, are not available in all cars and are expensive to retrofit for older vehicles. This paper describes how smartphones, such as the iPhone and Google Android platforms, can automatically detect traffic accidents using accelerometers and acoustic data, immediately notify a central emergency dispatch server after an accident, and provide situational awareness through photographs, GPS coordinates, VOIP communication channels, and accident data recording. This paper provides the following contributions to the study of detecting traffic accidents via smartphones: (1) we present a formal model for accident detection that combines sensors and context data, (2) we show how smartphone sensors, network connections, and web services can be used to provide situational awareness to first responders, and (3) we provide empirical results demonstrating the efficacy of different approaches employed by smartphone accident detection systems to prevent false positives.
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paper_title: VTrack: accurate, energy-aware road traffic delay estimation using mobile phones

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Traffic delays and congestion are a major source of inefficiency, wasted fuel, and commuter frustration. Measuring and localizing these delays, and routing users around them, is an important step towards reducing the time people spend stuck in traffic. As others have noted, the proliferation of commodity smartphones that can provide location estimates using a variety of sensors---GPS, WiFi, and/or cellular triangulation---opens up the attractive possibility of using position samples from drivers' phones to monitor traffic delays at a fine spatiotemporal granularity. This paper presents VTrack, a system for travel time estimation using this sensor data that addresses two key challenges: energy consumption and sensor unreliability. While GPS provides highly accurate location estimates, it has several limitations: some phones don't have GPS at all, the GPS sensor doesn't work in "urban canyons" (tall buildings and tunnels) or when the phone is inside a pocket, and the GPS on many phones is power-hungry and drains the battery quickly. In these cases, VTrack can use alternative, less energy-hungry but noisier sensors like WiFi to estimate both a user's trajectory and travel time along the route. VTrack uses a hidden Markov model (HMM)-based map matching scheme and travel time estimation method that interpolates sparse data to identify the most probable road segments driven by the user and to attribute travel times to those segments. We present experimental results from real drive data and WiFi access point sightings gathered from a deployment on several cars. We show that VTrack can tolerate significant noise and outages in these location estimates, and still successfully identify delay-prone segments, and provide accurate enough delays for delay-aware routing algorithms. We also study the best sampling strategies for WiFi and GPS sensors for different energy cost regimes.
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paper_title: EmotionSense: a mobile phones based adaptive platform for experimental social psychology research

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Today's mobile phones represent a rich and powerful computing platform, given their sensing, processing and communication capabilities. Phones are also part of the everyday life of billions of people, and therefore represent an exceptionally suitable tool for conducting social and psychological experiments in an unobtrusive way. de the ability of sensing individual emotions as well as activities, verbal and proximity interactions among members of social groups. Moreover, the system is programmable by means of a declarative language that can be used to express adaptive rules to improve power saving. We evaluate a system prototype on Nokia Symbian phones by means of several small-scale experiments aimed at testing performance in terms of accuracy and power consumption. Finally, we present the results of real deployment where we study participants emotions and interactions. We cross-validate our measurements with the results obtained through questionnaires filled by the users, and the results presented in social psychological studies using traditional methods. In particular, we show how speakers and participants' emotions can be automatically detected by means of classifiers running locally on off-the-shelf mobile phones, and how speaking and interactions can be correlated with activity and location measures.
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paper_title: Participatory Air Pollution Monitoring Using Smartphones

paper_content:

Air quality monitoring is extremely important as air pollution has a direct impact on human health. In this paper we introduce a low-power and low-cost mobile sensing system for participatory air quality monitoring. In contrast to traditional stationary air pollution monitoring stations, we present the design, implementation, and evaluation of GasMobile, a small and portable measurement system based on o-the-shelf components and suited to be used by a large number of people. Vital to the success of participatory sensing applications is a high data quality. We improve measurement accuracy by (i) exploiting sensor readings near governmental measurement stations to keep sensor calibration up to date and (ii) analyzing the eect of mobility on the accuracy of the sensor readings to give user advice on measurement execution. Finally, we show that it is feasible to use GasMobile to create collective high-resolution air pollution maps.
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paper_title: Automatic Collection of Fuel Prices from a Network of Mobile Cameras

paper_content:

It is an undeniable fact that people want information. Unfortunately, even in today's highly automated society, a lot of the information we desire is still manually collected. An example is fuel prices where websites providing fuel price information either send their workers out to manually collect the prices or depend on volunteers manually relaying the information. This paper proposes a novel application of wireless sensor networks to automatically collect fuel prices from camera images of road-side price board (billboard) of service (or gas) stations. Our system exploits the ubiquity of mobile phones that have cameras as well as users contributing and sharing data. In our proposed system, cameras of contributing users will be automatically triggered when they get close to a service station. These images will then be processed by computer vision algorithms to extract the fuel prices. In this paper, we will describe the system architecture and present results from our computer vision algorithms. Based on 52 images, our system achieves a hit rate of 92.3% for correctly detecting the fuel price board from the image background and reads the prices correctly in 87.7% of them. To the best of our knowledge, this is the first instance of a sensor network being used for collecting consumer pricing information.
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paper_title: HazeWatch: A participatory sensor system for monitoring air pollution in Sydney

paper_content:

This paper describes our experiences with building and deploying a low-cost participatory system for urban air pollution monitoring in Sydney. Though air pollution imposes significant health costs on the urban community globally, today's published data on pollution concentrations is spatially too sparse, and does not allow for sufficiently accurate estimation of exposures for (potentially mobile) individuals in order to make medical inferences. The HazeWatch project described in this paper uses several low-cost mobile sensor units attached to vehicles to measure air pollution concentrations, and users' mobile phones to tag and upload the data in real time. The greater spatial granularity of data thus collected enables creation of pollution maps of metropolitan Sydney viewable in real-time over the web, as well as personalized apps that show the individual's exposure history and allow for route planning to reduce future exposure. We share the insights we obtained from building and trialling such a system in Sydney, and highlight challenges that can be addressed collaboratively by groups developing similar systems world-wide.
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paper_title: Creek watch: pairing usefulness and usability for successful citizen science

paper_content:

Citizen science projects can collect a wealth of scientific data, but that data is only helpful if it is actually used. While previous citizen science research has mostly focused on designing effective capture interfaces and incentive mechanisms, in this paper we explore the application of HCI methods to ensure that the data itself is useful. To provide a focus for this exploration we designed and implemented Creek Watch, an iPhone application and website that allow volunteers to report information about waterways in order to aid water management programs. Working with state and local officials and private groups involved in water monitoring, we conducted a series of contextual inquiries to uncover what data they wanted, what data they could immediately use, and how to most effectively deliver that data to them. We iteratively developed the Creek Watch application and website based on our findings and conducted evaluations of it with both contributors and consumers of water data, including scientists at the city water resources department. Our study reveals that the data collected is indeed useful for their existing practices and is already in use in water and trash management programs. Our results suggest the application of HCI methods to design the data for the end users is just as important as their use in designing the user interface.
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paper_title: GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation

paper_content:

Although a large number of WiFi fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.
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paper_title: SoundSense: scalable sound sensing for people-centric applications on mobile phones

paper_content:

Top end mobile phones include a number of specialized (e.g., accelerometer, compass, GPS) and general purpose sensors (e.g., microphone, camera) that enable new people-centric sensing applications. Perhaps the most ubiquitous and unexploited sensor on mobile phones is the microphone - a powerful sensor that is capable of making sophisticated inferences about human activity, location, and social events from sound. In this paper, we exploit this untapped sensor not in the context of human communications but as an enabler of new sensing applications. We propose SoundSense, a scalable framework for modeling sound events on mobile phones. SoundSense is implemented on the Apple iPhone and represents the first general purpose sound sensing system specifically designed to work on resource limited phones. The architecture and algorithms are designed for scalability and Soundsense uses a combination of supervised and unsupervised learning techniques to classify both general sound types (e.g., music, voice) and discover novel sound events specific to individual users. The system runs solely on the mobile phone with no back-end interactions. Through implementation and evaluation of two proof of concept people-centric sensing applications, we demostrate that SoundSense is capable of recognizing meaningful sound events that occur in users' everyday lives.
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paper_title: Mobile Social Networks: Architectures, Social Properties, and Key Research Challenges

paper_content:

Mobile social networks (MSNs) are mobile communication systems focusing not only on the behaviour but also on the social needs of the users. In a broader view all mobile systems used by people in their everyday lives can be characterized as MSNs, since all interactions taking place follow social rather than random patterns. Whether the deployed communication system takes into account the social background of the underlying network, depends on its form and capabilities. This article presents a review of the relevant work published in MSNs. Initially the basic architectures and components are summarized. Then the basic social properties of the network, as found in the key literature, are extensively examined. These properties are the main source of inspiration for new MSN protocols and applications, especially for non centralized systems. Finally the key research problems and the open issues in the area are presented, including future applications and privacy concerns.
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paper_title: WhozThat? evolving an ecosystem for context-aware mobile social networks

paper_content:

One of the most compelling social questions, which until now was left unanswered by current technology, is "Who's that?" This question is usually asked about a new unfamiliar person in a social setting who piques your curiosity. This article presents WhozThat, a system that ties together online social networks with mobile smart- phones to answer this common and essential social question. WhozThat builds an infrastructure that shares social networking IDs locally, using wireless technology, while also leveraging a wireless connection to the Internet's social networks to bind identity with location. In addition, WhozThat offers an entire ecosystem on which increasingly complex context-aware applications can be built; that is, once the environment knows who one is, the environment can adapt its content based on the individual's identity or even the collective tastes of a local group. We describe a prototype of the basic WhozThat system and also describe a more advanced service we built, a context-aware music player. We also discuss security and privacy issues introduced by mobile social networks.
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paper_title: MobiClique: middleware for mobile social networking

paper_content:

We consider a mobile ad hoc network setting where Bluetooth enabled mobile devices communicate directly with other devices as they meet opportunistically. We design and implement a novel mobile social networking middleware named MobiClique. MobiClique forms and exploits ad hoc social networks to disseminate content using a store-carry-forward technique. Our approach distinguishes itself from other mobile social software by removing the need for a central server to conduct exchanges, by leveraging existing social networks to bootstrap the system, and by taking advantage of the social network overlay to disseminate content. We also propose an open API to encourage third-party application development. We discuss the system architecture and three example applications. We show experimentally that MobiClique successfully builds and maintains an ad hoc social network leveraging contact opportunities between friends and people sharing interest(s) for content exchanges. Our experience also provides insight into some of the key challenges and short-comings that researchers face when designing and deploying similar systems.
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paper_title: MoVi: mobile phone based video highlights via collaborative sensing

paper_content:

Sensor networks have been conventionally defined as a network of sensor motes that collaboratively detect events and report them to a remote monitoring station. This paper makes an attempt to extend this notion to the social context by using mobile phones as replacement of motes. We envision a social application where mobile phones collaboratively sense their ambience and recognize socially "interesting" events. The phone with a good view of the event triggers a video recording, and later, the video-clips from different phones are "stitched" into a video highlights of the occasion. We observe that such a video highlights is akin to the notion of event coverage in conventional sensor networks, only the notion of "event" has changed from physical to social. We have built a Mobile Phone based Video Highlights system (MoVi) using Nokia phones and iPod Nanos, and have experimented in real-life social gatherings. Results show that MoVi-generated video highlights (created offline) are quite similar to those created manually, (i.e., by painstakingly editing the entire video of the occasion). In that sense, MoVi can be viewed as a collaborative information distillation tool capable of filtering events of social relevance.
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paper_title: SPA: a smart phone assisted chronic illness self-management system with participatory sensing

paper_content:

The medical system has not been able to effectively adapt to the dramatic transformation in public health challenges; from acute to chronic and lifestyle-related illnesses. Although acute illnesses can be treated successfully in an office or hospital, chronic illnesses comprise the bulk of health care needs and require a very different approach. There is overwhelming consensus that the prevention and treatment of cardiovascular disease, diabetes, hypertension, chronic pain, obesity, asthma, HIV, and many other chronic illnesses require substantial patient self-management [2].
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paper_title: SociableSense: exploring the trade-offs of adaptive sampling and computation offloading for social sensing

paper_content:

The interactions and social relations among users in workplaces have been studied by many generations of social psychologists. There is evidence that groups of users that interact more in workplaces are more productive. However, it is still hard for social scientists to capture fine-grained data about phenomena of this kind and to find the right means to facilitate interaction. It is also difficult for users to keep track of their level of sociability with colleagues. While mobile phones offer a fantastic platform for harvesting long term and fine grained data, they also pose challenges: battery power is limited and needs to be traded-off for sensor reading accuracy and data transmission, while energy costs in processing computationally intensive tasks are high. In this paper, we propose SociableSense, a smart phones based platform that captures user behavior in office environments, while providing the users with a quantitative measure of their sociability and that of colleagues. We tackle the technical challenges of building such a tool: the system provides an adaptive sampling mechanism as well as models to decide whether to perform computation of tasks, such as the execution of classification and inference algorithms, locally or remotely. We perform several micro-benchmark tests to fine-tune and evaluate the performance of these mechanisms and we show that the adaptive sampling and computation distribution schemes balance trade-offs among accuracy, energy, latency, and data traffic. Finally, by means of a social psychological study with ten participants for two working weeks, we demonstrate that SociableSense fosters interactions among the participants and helps in enhancing their sociability.
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paper_title: Ear-phone: an end-to-end participatory urban noise mapping system

paper_content:

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end participatory urban noise mapping system called Ear-Phone. Ear-Phone, for the first time, leverages Compressive Sensing to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95 and HP iPAQ mobile devices, also addresses the challenge of collecting accurate noise pollution readings at a mobile device. Extensive simulations and outdoor experiments demonstrate that Ear-Phone is a feasible platform to assess noise pollution, incurring reasonable system resource consumption at mobile devices and providing high reconstruction accuracy of the noise map.
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paper_title: Development of Mobile Radiation Monitoring System Utilizing Smartphone and Its Field Tests in Fukushima

paper_content:

We developed a series of inexpensive but accurate and mobile radiation detectors, which we named Pocket Geiger (POKEGA), to address the desire of ordinary people to own a radiation detector following the March 2011 Daiichi Nuclear Power Plant accidents in Fukushima, Japan. To reduce costs while maintaining accuracy and flexibility, we used a combination of a p-i-n photodiode detector connected to a smartphone via a microphone cable. The detector circuit design is optimized for simplicity and low cost, whereas the smartphone software application is tasked with handling the complex processing required. Furthermore, the device also used the GPS and networking capabilities of the smartphone for logging and data sharing. The 137Cs measurement range for a POKEGA-equipped smartphone is approximately from 0.05 to 10 mSv/h, which covers most radiation levels measured in Japan. Approximately 12000 POKEGA units were shipped in the six months following its release, and 2000 users have joined a Facebook community where they report measurement results and discuss hardware and software improvements. In parallel, we have addressed practical problems for POKEGA, such as vibration noise, energy consumption, and operating temperature, by conducting field tests in the Fukushima evacuation zone. The POKEGA series has been improved by solving such issues. This article reports on a new style of pragmatic sensor networking methodology, from the aspects of emergency response engineering, open-sourced development, and consumer-generated measurements.
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paper_title: Nericell: rich monitoring of road and traffic conditions using mobile smartphones

paper_content:

We consider the problem of monitoring road and traffic conditions in a city. Prior work in this area has required the deployment of dedicated sensors on vehicles and/or on the roadside, or the tracking of mobile phones by service providers. Furthermore, prior work has largely focused on the developed world, with its relatively simple traffic flow patterns. In fact, traffic flow in cities of the developing regions, which comprise much of the world, tends to be much more complex owing to varied road conditions (e.g., potholed roads), chaotic traffic (e.g., a lot of braking and honking), and a heterogeneous mix of vehicles (2-wheelers, 3-wheelers, cars, buses, etc.). To monitor road and traffic conditions in such a setting, we present Nericell, a system that performs rich sensing by piggybacking on smartphones that users carry with them in normal course. In this paper, we focus specifically on the sensing component, which uses the accelerometer, microphone, GSM radio, and/or GPS sensors in these phones to detect potholes, bumps, braking, and honking. Nericell addresses several challenges including virtually reorienting the accelerometer on a phone that is at an arbitrary orientation, and performing honk detection and localization in an energy efficient manner. We also touch upon the idea of triggered sensing, where dissimilar sensors are used in tandem to conserve energy. We evaluate the effectiveness of the sensing functions in Nericell based on experiments conducted on the roads of Bangalore, with promising results.
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paper_title: Mobile crowd sensing in space weather monitoring: the mahali project

paper_content:

Space weather refers to the conditions and evolution of Earth's near space environment including electron density variations in the ionosphere. This environment is influenced by both the Sun and terrestrial processes, and has an impact on communications, navigation, and terrestrial power systems. The recent discovery of clear signatures in the ionosphere related to tsunamis and earthquakes suggests that the ionosphere itself may serve as a valuable and versatile sensor, registering many types of Earth- and space-based phenomena. To realize this potential, ionospheric electron density must be monitored through a dense wide-area sensor mesh that is expensive to realize with traditional deployments and observation techniques. Crowdsourcing can help pursue this novel direction by providing new capabilities, including an increase in the number of sensors as well as expanding data transport capabilities through participating devices that act as relays. This article describes the Mahali project, which is currently at the beginning of exploring these promising techniques. Mahali uses GPS signals that penetrate the ionosphere for science rather than positioning. A large number of ground-based sensors will be able to feed data through mobile devices into a cloud-based processing environment, enabling a tomographic analysis of the global ionosphere at unprecedented resolution and coverage. This novel approach brings the exploitation of the ionosphere as a global earth system sensor technologically and economically within reach.
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paper_title: Common Sense: participatory urban sensing using a network of handheld air quality monitors

paper_content:

Poor air quality is a global health issue, causing serious problems like asthma, cancer, and heart disease around the world. Earlier this decade, the World Health Organization estimated that three million people die each year from the effects of air pollution [6]. Unfortunately, while variations in air quality are significant, today's air quality monitors are very sparsely deployed. To address this visibility gap, the Common Sense project is developing participatory sensing systems that allow individuals to measure their personal exposure, groups to aggregate their members' exposure, and activists to mobilize grassroots community action.
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paper_title: Sensing meets mobile social networks: the design, implementation and evaluation of the CenceMe application

paper_content:

We present the design, implementation, evaluation, and user ex periences of theCenceMe application, which represents the first system that combines the inference of the presence of individuals using off-the-shelf, sensor-enabled mobile phones with sharing of this information through social networking applications such as Facebook and MySpace. We discuss the system challenges for the development of software on the Nokia N95 mobile phone. We present the design and tradeoffs of split-level classification, whereby personal sensing presence (e.g., walking, in conversation, at the gym) is derived from classifiers which execute in part on the phones and in part on the backend servers to achieve scalable inference. We report performance measurements that characterize the computational requirements of the software and the energy consumption of the CenceMe phone client. We validate the system through a user study where twenty two people, including undergraduates, graduates and faculty, used CenceMe continuously over a three week period in a campus town. From this user study we learn how the system performs in a production environment and what uses people find for a personal sensing system.
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paper_title: HealthAware: Tackling obesity with health aware smart phone systems

paper_content:

Obesity prevention requires individuals to have healthy eating and physical activity awareness in their daily lives. New technological products have been explored to help monitoring individual health behaviors. Smart phones are ubiq­uitous hand-held tools with rich embedded sensors and limited computing power. A smart phone is a prospective candidate to integrate physical exercise analysis and food intake monitoring. This paper presents a novel health-aware smart phone system (HealthAware) which utilizes the embedded accelerometer to monitor daily physical activities and the built-in camera to analyze food items. The system presents the user's physical activity counts at real time to remind how much daily activity is needed to keep healthy. The user takes pictures of food items and the system provides health related information regarding to the food item intakes. The system is composed of an on-device database which holds the user specific data and food item information. HealthAware is a real time practical system to prevent obesity by enhancing individual daily healthy behavior awareness.
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paper_title: Andwellness: An open mobile system for activity and experience sampling

paper_content:

Advances in mobile phone technology have allowed phones to become a convenient platform for real-time assessment of a participants health and behavior. AndWellness, a personal data collection system, uses mobile phones to collect and analyze data from both active, triggered user experience samples and passive logging of onboard environmental sensors. The system includes an application that runs on Android based mobile phones, server software that manages deployments and acts as a central repository for data, and a dashboard front end for both participants and researchers to visualize incoming data in real-time. Our system has gone through testing by researchers in preparation for deployment with participants, and we describe an initial qualitative study plus several planned future studies to demonstrate how our system can be used to better understand a user's health related habits and observations.
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paper_title: SensTrack: Energy-Efficient Location Tracking With Smartphone Sensors

paper_content:

Nowadays, as smartphones are becoming more and more powerful, applications providing location based services have been increasingly popular. Many, if not all, smartphones are equipped with a powerful sensor set (GPS, WiFi, the acceleration sensor, the orientation sensor, etc.), which makes them capable of accomplishing complicated tasks. Unfortunately, as the core enabler of most location tracking applications on smartphones, GPS incurs an unacceptable energy cost that can cause the complete battery drain within a few hours. Although GPS is often preferred over its alternatives, the coverage areas of GPS are still limited (GPS typically cannot function indoors). To this end, our goal in this paper is to improve the energy-efficiency of traditional location tracking service as well as to expand its coverage areas. In this paper, we introduce SensTrack, a location tracking service that leverages the sensor hints on the smartphone to reduce the usage of GPS. SensTrack selectively executes a GPS sampling using the information from the acceleration and orientation sensors and switches to the alternate location sensing method based on WiFi when users move indoors. A machine learning technique, Gaussian process regression, is then employed to reconstruct the trajectory from the recorded location samples. We implemented a prototype on an Android smartphone that can sample the related sensors during the user's movement and collect the sensor data for further processing on PCs. Evaluation on traces from real users demonstrates that SensTrack can significantly reduce the usage of GPS and still achieve a high tracking accuracy.
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paper_title: Crowdsensing Framework for Monitoring Bridge Vibrations Using Moving Smartphones

paper_content:

Cities are encountering extensive deficits in infrastructure service while they are experiencing rapid technological advancements and overhauls in transportation systems. Standard bridge evaluation methods rely on visual inspections, which are infrequent and subjective, ultimately affecting the structural assessments on which maintenance plans are based. The operational behavior of a bridge must be observed more regularly and over an extended period in order to sufficiently track its condition and avoid unexpected rehabilitation. Mobile sensor networks are conducive to monitoring bridges vibrations routinely, with benefits that have been demonstrated in recent structural health monitoring (SHM) research. Though smartphone accelerometers are imperfect sensors, they can contribute valuable information to SHM, especially when aggregated, e.g., via crowdsourcing. In an application on the Harvard Bridge (Boston, MA), it is shown that acceleration data collected using smartphones in moving vehicles contained consistent and significant indicators of the first three modal frequencies of the bridge. In particular, the results became more precise when informatics from several smartphone datasets were combined. This evidence is the first to support the hypothesis that smartphone data, collected within vehicles passing over a bridge, can be used to detect several modal frequencies of the bridge. The result defines an opportunity for local governments to make partnerships that encourage the collection of low-cost bridge vibration data, which can contribute to more effective management and informed decision-making.
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paper_title: Creek watch: pairing usefulness and usability for successful citizen science

paper_content:

Citizen science projects can collect a wealth of scientific data, but that data is only helpful if it is actually used. While previous citizen science research has mostly focused on designing effective capture interfaces and incentive mechanisms, in this paper we explore the application of HCI methods to ensure that the data itself is useful. To provide a focus for this exploration we designed and implemented Creek Watch, an iPhone application and website that allow volunteers to report information about waterways in order to aid water management programs. Working with state and local officials and private groups involved in water monitoring, we conducted a series of contextual inquiries to uncover what data they wanted, what data they could immediately use, and how to most effectively deliver that data to them. We iteratively developed the Creek Watch application and website based on our findings and conducted evaluations of it with both contributors and consumers of water data, including scientists at the city water resources department. Our study reveals that the data collected is indeed useful for their existing practices and is already in use in water and trash management programs. Our results suggest the application of HCI methods to design the data for the end users is just as important as their use in designing the user interface.
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paper_title: SafeStreet: An automated road anomaly detection and early-warning system using mobile crowdsensing

paper_content:

In this work, we present SafeStreet, an automated road anomaly detection and early warning system. A pilot of SafeStreet was conducted by deploying the SafeStreet mobile app on 1800 cabs in Mumbai and Bengaluru. We discuss some of the design considerations and early results from our pilot study.
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paper_title: Direction-sensitive smart monitoring of structures using heterogeneous smartphone sensor data and coordinate system transformation

paper_content:

Mobile, heterogeneous, and smart sensor networks produce pervasive structural health monitoring (SHM) information. With various embedded sensors, smartphones have emerged to innovate SHM by empowering citizens to serve as sensors. By default, smartphones meet the fundamental smart sensor criteria, thanks to the built-in processor, memory, wireless communication units and mobile operating system. SHM using smartphones, however, faces technical challenges due to citizen-induced uncertainties, undesired sensor-structure integration, and lack of control over the sensing platform. Previously, the authors presented successful applications of smartphone accelerometers for structural vibration measurement and proposed a monitoring framework under citizen-induced spatiotemporal uncertainties. This study aims at extending the capabilities of smartphone-based SHM with a special focus on the lack of control over the sensor (i.e., the phone) positioning by citizens resulting in unknown sensor orientations. Using smartphone gyroscope, accelerometer, and magnetometer; instantaneous sensor orientation can be obtained with respect to gravitational and magnetic north directions. Using these sensor data, mobile operating system frameworks return processed features such as attitude and heading that can be used to correct misaligned sensor signals. For this purpose, a coordinate transformation procedure is proposed and illustrated on a two-story laboratory structural model and real-scale bridges with various sensor positioning examples. The proposed method corrects the sensor signals by tracking their orientations and improves measurement accuracy. Moreover, knowing structure's coordinate system a priori, even the data from arbitrarily positioned sensors can automatically be transformed to the structural coordinates. In addition, this paper also touches some secondary mobile and heterogeneous data issues including imperfect sampling and geolocation services. The coordinate system transformation methods proposed in this study can be implemented in other non-smartphone-based SHM systems as long as similar instrumentation is available.
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paper_title: Evaluating the Reliability of Phones as Seismic Monitoring Instruments

paper_content:

Emergency responders must “see” the effects of an earthquake clearly and rapidly for effective response. This paper presents a novel use of cell phone and information technology to measure ground motion intensity parameters. The phone sensor is an imperfect device and has a limited operational range. Thus, shake table tests were performed to evaluate their reliability as seismic monitoring instruments. Representative handheld devices, either rigidly connected to the table or free to move, measured shaking intensity parameters well. Bias in 5%-damped spectral accelerations measured by phones was less than 0.05 and 0.2 [log(g)] during one-dimensional (1-D) and three-dimensional (3-D) shaking in frequencies ranging from 1 Hz to 10 Hz. They did tend to overestimate the Arias Intensity, but this error declined for stronger motions with larger signal-to-noise ratios. With these ubiquitous measurement devices, a more accurate and rapid portrayal of the damage distribution during an earthquake can be provided.
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paper_title: Mobile Phones as Seismologic Sensors: Automating Data Extraction for the iShake System

paper_content:

There are a variety of approaches to seismic sensing, which range from collecting sparse measurements with high-fidelity seismic stations to non-quantitative, post-earthquake surveys. The sparse nature of the high-fidelity stations and the inaccuracy of the surveys create the need for a high-density, semi-quantitative approach to seismic sensing. To fill this void, the UC Berkeley iShake project designed a mobile client-backend server architecture that uses sensor-equipped mobile devices to measure earthquake ground shaking. iShake provides the general public with a service to more easily contribute more quantitatively significant data to earthquake research by automating the data collection and reporting mechanisms via the iShake mobile application. The devices act as distributed sensors that enable measurements to be taken and transmitted with a cellular network connection. Shaking table testing was used to assess the quality of the measurements obtained from the iPhones and iPods on a benchmark of 150 ground motions. Once triggered by a shaking event, the devices transmit sensor data to a backend server for further processing. After a seismic event is verified by high-fidelity stations, filtering algorithms are used to detect falling phones, as well as device-specific responses to the event. A method was developed to determine the absolute orientation of a device to estimate the direction of first motion of a seismic event. A “virtual earthquake” pilot test was conducted on the UC Berkeley campus to verify the operation of the iShake system. By designing and fully implementing a system architecture, developing signal processing techniques unique to mobile sensing, and conducting shaking table tests to confirm the validity of the sensing platform, the iShake project serves as foundational work for further studies in seismic sensing on mobile devices.
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paper_title: Asfault: A low-cost system to evaluate pavement conditions in real-time using smartphones and machine learning

paper_content:

Abstract Modern smartphones have a large variety of built-in sensors that can measure different information about users and the environment around them. Given the increasing popularity of these devices, their high processing power, and the ability to transfer data over wireless networks, different smartphone-based applications have emerged in the last years to solve old problems with new approaches more efficiently and cheaply. One example is the assessment and monitoring of asphalt quality. This task has been done manually by experts since the 1930s, and with the help of expensive equipment since the 1960s. Currently, we are experiencing the emergence of next-generation tools to perform this monitoring with smartphones, significantly reducing costs, time, and effort of experts. However, there is a trade-off between the costs and precision of smartphone sensors, requiring the use of sophisticated software solutions. In this paper, we propose Asfault, a low-cost system to evaluate and monitor road pavement conditions in real-time using smartphone sensors and machine learning algorithms. The system is composed of an Android application responsible for doing automatic evaluations and a web application that aims to show the evaluations in an informative way. We propose to employ accelerometer sensors to measure the vehicle vibration while driving and use this data to evaluate the pavement conditions. Asfault achieves a classification performance superior to 90% in a 5-class problem considering the following road qualities: Good, Average, Fair, and Poor, as well the occurrence of obstacles in the road. Our system is publicly available for use and could be useful for practitioners responsible for urban and highway maintenance, as well for regular drivers in the planning of better routes based on the pavement quality and comfort of the travel.
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paper_title: CrowdMonitor: Mobile Crowd Sensing for Assessing Physical and Digital Activities of Citizens during Emergencies

paper_content:

Emergencies such as the 2013 Central European flood or the 2013 typhoon Haiyan in Philippines have shown how citizens can organize themselves and coordinate private relief activities. These activities can be found in (physical) groups of affected people, but also within (digital) social media communities. There is an evident need, however, for a clearer picture of what exactly is going on to be available for use by the official emergency services: to enlist them, to keep them safe, to support their efforts and to avoid needless duplications or conflicts. Aligning emergency services and volunteer activities is, then, crucial. In this paper we present a mobile crowd sensing based concept, which was designed as well as implemented as the application CrowdMonitor and facilitates the detection of physical and digital activities and the assignment of specific tasks to citizens. Finally we outline the findings of its evaluation.
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paper_title: Sociability-Driven Framework for Data Acquisition in Mobile Crowdsensing Over Fog Computing Platforms for Smart Cities

paper_content:

Smart cities exploit the most advanced information technologies to improve and add value to existing public services. Having citizens involved in the process through mobile crowdsensing (MCS) augments the capabilities of the platform without enquiring additional costs. In this paper, we propose a novel framework for data acquisition in MCS deployed over a fog computing platform which facilitates a number of key operations including user recruitment and task completion. Proper data acquisition minimizes the monetary expenditure the platform sustains to recruit and compensate users as well as the energy they spend to sense and deliver data. We propose a new user recruitment policy called Distance, Sociability, Energy (DSE). This policy exploits three criteria: (i) spatial distance between users and tasks, (ii) user sociability, which is an estimate of the willingness of users to contribute to sensing tasks, and (iii) remaining battery charge of the devices. Performance evaluation is conducted in a real urban environment for a large number of participants with new metrics assessing the efficiency of recruitment and the accuracy of task completion. Results reveal that the average number of recruited users improves by nearly 20 percent if compared to policies using only spatial distance as selection criterion.
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paper_title: Survey on fog computing : architecture , key technologies , applications and open issues

paper_content:

The emergence of Internet of Things (IoT) has enabled the interconnection and intercommunication among massive ubiquitous things, which caused an unprecedented generation of huge and heterogeneous amount of data, known as data explosions. On the other hand, although that cloud computing has served as an efficient way to process and store these data, however, challenges, such as the increasing demands of real time or latency-sensitive applications and the limitation of network bandwidth, still cannot be solved by using only cloud computing. Therefore, a new computing paradigm, known as fog computing, has been proposed as a complement to the cloud solution. Fog computing extends the cloud services to the edge of network, and makes computation, communication and storage closer to edge devices and end-users, which aims to enhance low-latency, mobility, network bandwidth, security and privacy. In this paper, we will overview and summarize fog computing model architecture, key technologies, applications, challenges and open issues. Firstly, we will present the hierarchical architecture of fog computing and its characteristics, and compare it with cloud computing and edge computing to emphasize the similarities and differences. Then, the key technologies like computing, communication and storage technologies, naming, resource management, security and privacy protection are introduced to present how to support its deployment and application in a detailed manner. Several application cases like health care, augmented reality, brain machine interface and gaming, smart environments and vehicular fog computing are also presented to further explain fog computing application scenarios. Finally, based on the observation, we propose some challenges and open issues which are worth further in-depth study and research in fog computing development.
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paper_title: Human-Enabled Edge Computing: Exploiting the Crowd as a Dynamic Extension of Mobile Edge Computing

paper_content:

The MEC vision leverages the availability of powerful and low-cost middleboxes, statically deployed at suitable edges of the network and acting as local proxies for the centralized cloud backbone; this potentially enables, among other things, better scalability and better reactivity in the interaction with mobile nodes via local control decisions and actuation. MEC has already been proposed as an enabler for several Internet of Things and cyber-physical systems application scenarios, and also mutual benefits due to the integration of MEC and mobile crowdsensing (MCS). The article originally proposes human-driven edge computing (HEC) as a new model to ease the provisioning and to extend the coverage of traditional MEC solutions. From a methodological perspective, we show how it is possible to exploit MCS i) to support the effective deployment of fixed MEC (FMEC) proxies and ii) to further extend their coverage through the introduction of impromptu and human-enabled mobile MEC (M2EC) proxies. In addition, we describe how we have implemented these novel concepts in the MCS ParticipAct platform through the integration of the MEC Elijah platform in the ParticipAct living lab, an ongoing MCS real-world experiment that involved about 170 students at the University of Bologna for more than two years. Reported experimental results quantitatively show the effectiveness of the proposed techniques in elastically scaling the load at edge nodes according to runtime provisioning needs.
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paper_title: Exploiting Mobile Big Data: Sources, Features, and Applications

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The worldwide rollout of 4G LTE mobile communication networks has accelerated the proliferation of the mobile Internet and spurred a new wave of mobile applications on smartphones. This new wave has provided mobile operators an enormous opportunity to collect a huge amount of data to monitor the technical and transactional aspects of their networks. Recent research on mobile big data mining have shown its great potential for diverse purposes ranging from improving traffic management, enabling personal and contextual services, to monitoring city dynamics and so on. The mobile big data research has a multi-disciplinary nature that demands distinct knowledge from mobile communications, signal processing, and data mining. The research field of mobile big data has emerged quickly in recent years, but is somewhat fragmented. This article aims to provide an integrated picture of this emerging field to bridge multiple disciplines and hopefully to inspire future research.
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paper_title: Blockchains and Smart Contracts for the Internet of Things

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Motivated by the recent explosion of interest around blockchains, we examine whether they make a good fit for the Internet of Things (IoT) sector. Blockchains allow us to have a distributed peer-to-peer network where non-trusting members can interact with each other without a trusted intermediary, in a verifiable manner. We review how this mechanism works and also look into smart contracts—scripts that reside on the blockchain that allow for the automation of multi-step processes. We then move into the IoT domain, and describe how a blockchain-IoT combination: 1) facilitates the sharing of services and resources leading to the creation of a marketplace of services between devices and 2) allows us to automate in a cryptographically verifiable manner several existing, time-consuming workflows. We also point out certain issues that should be considered before the deployment of a blockchain network in an IoT setting: from transactional privacy to the expected value of the digitized assets traded on the network. Wherever applicable, we identify solutions and workarounds. Our conclusion is that the blockchain-IoT combination is powerful and can cause significant transformations across several industries, paving the way for new business models and novel, distributed applications.
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paper_title: FINE: A Framework for Distributed Learning on Incomplete Observations for Heterogeneous Crowdsensing Networks

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In recent years, there has been a wide range of applications of crowdsensing in mobile social networks and vehicle networks. As centralized learning methods lead to unreliabitlity of data collection, high cost of central server, and concern of privacy, one important problem is how to carry out an accurate distributed learning process to estimate parameters of an unknown model in crowdsensing. Motivated by this, we present the design, analysis, and evaluation of FINE, a distributed learning framework for incomplete-data and non-smooth estimation. Our design, devoted to develop a feasible framework that efficiently and accurately learns the parameters in crowdsensing networks, well generalizes the previous learning methods in which it supports heterogeneous dimensions of data records observed by different nodes, as well as minimization based on non-smooth error functions. In particular, FINE uses a novel distributed record completion algorithm that allows each node to obtain the global consensus by an efficient communication with neighbors, and a distributed dual average algorithm that achieves the efficiency of minimizing non-smooth error functions. Our analysis shows that all these algorithms converge, of which the convergence rates are also derived to confirm their efficiency. We evaluate the performance of our framework with experiments on synthetic and real-world networks.
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paper_title: 5G-Enabled Tactile Internet

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The long-term ambition of the Tactile Internet is to enable a democratization of skill, and how it is being delivered globally. An integral part of this is to be able to transmit touch in perceived real-time, which is enabled by suitable robotics and haptics equipment at the edges, along with an unprecedented communications network. The fifth generation (5G) mobile communications systems will underpin this emerging Internet at the wireless edge. This paper presents the most important technology concepts, which lay at the intersection of the larger Tactile Internet and the emerging 5G systems. The paper outlines the key technical requirements and architectural approaches for the Tactile Internet, pertaining to wireless access protocols, radio resource management aspects, next generation core networking capabilities, edge-cloud, and edge-AI capabilities. The paper also highlights the economic impact of the Tactile Internet as well as a major shift in business models for the traditional telecommunications ecosystem.
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paper_title: Greenhouse gas emission reduction options for cities: Finding the “Coincidence of Agendas” between local priorities and climate change mitigation objectives

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Abstract Cities are the major contributors to global greenhouse gas (GHG) emissions. They account for about 75% of global energy consumption and up to 80% of global greenhouse gas emissions. With the ongoing rapid increase in urban population, expansion of middle class in urban centers in developing countries, and availability of cheaper vehicles such as Tata Nano and Bajaj RE60 in India, the demand for energy and associated emissions from cities are expected to grow rapidly. Though cities are in a better position to mitigate climate change, it does not necessary mean that there is a willingness on their part to capitalize on these mitigation opportunities. Climate change mitigation is not the priority for them because they face a number of competing priorities including local economic growth and development and service delivery. This paper suggests a range of policy tools that can help cities achieve both local priorities as well as reduce emissions, including GHGs. The suggested policies will be effective when used synergistically.
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paper_title: Scaling Millimeter-Wave Networks to Dense Deployments and Dynamic Environments

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Millimeter-wave (mmWave) communications have emerged as one of the most promising options to vastly increase wireless data rates due to the high bandwidth they offer. Given the high path loss at mmWave frequencies, such systems require directional antennas to achieve a good communication range. Thus, the communicating devices need to align the beam directions of their mmWave antennas. Due to the high penetration loss, the paths between the antennas also need to be free of blocking obstacles. This makes an efficient and reliable operation of mmWave networks in dynamic environments very challenging. At the same time, the directionality reduces interference and allows to scale these networks to much higher access point and device densities. In this paper, we discuss the above-mentioned challenges and present techniques that allow mmWave networks to scale to high-density deployments, to adapt to dynamic and mobile environments, and to consistently achieve high data rates. This includes learning the environment to find different propagation paths, reacting timely to channel impairments such as blockage, and integrating mmWave networks with networks operating at a lower frequency for robustness. A key ingredient to enable these forms of adaptivity is the use of location information. Such mechanisms then turn a collection of very-high-speed but brittle mmWave links into an efficient, low-latency, and reliable network.
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paper_title: Scalable Energy-Efficient Distributed Data Analytics for Crowdsensing Applications in Mobile Environments

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We are witnessing a new revolution in computing and communication involving symbiotic networks of people (social networks), intelligent devices, smart mobile computing, and communication devices that will form cyber-physical social systems. The emergence of intelligent devices with monitoring, sensing, and actuation capabilities referred to as Internet of Things and social networks have increased the popularity of novel social applications such as crowdsourcing and crowdsensing. The upsurge of such applications has fostered the need for scalable cost-efficient platforms that can enable distributed data analytics. In this paper, we propose CARDAP, a scalable, energy-efficient, generic and extensible component-based distributed data analytics platform for mobile crowdsensing (MCS) applications. CARDAP incorporates on-the-move activity recognition and a number of energy efficient data delivery strategies using real-time mobile data stream mining. We propose and develop theoretical cost models for typical crowdsensing application scenarios. Experimental evaluations of CARDAP using a proof-of-concept MCS scenario validate the theoretical cost model estimates and demonstrate the platform's ability to deliver significant benefits in energy, resource, and query processing efficiency.
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paper_title: 5G: A Tutorial Overview of Standards, Trials, Challenges, Deployment, and Practice

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There is considerable pressure to define the key requirements of 5G, develop 5G standards, and perform technology trials as quickly as possible. Normally, these activities are best done in series but there is a desire to complete these tasks in parallel so that commercial deployments of 5G can begin by 2020. 5G will not be an incremental improvement over its predecessors; it aims to be a revolutionary leap forward in terms of data rates, latency, massive connectivity, network reliability, and energy efficiency. These capabilities are targeted at realizing high-speed connectivity, the Internet of Things, augmented virtual reality, the tactile internet, and so on. The requirements of 5G are expected to be met by new spectrum in the microwave bands (3.3-4.2 GHz), and utilizing large bandwidths available in mm-wave bands, increasing spatial degrees of freedom via large antenna arrays and 3-D MIMO, network densification, and new waveforms that provide scalability and flexibility to meet the varying demands of 5G services. Unlike the one size fits all 4G core networks, the 5G core network must be flexible and adaptable and is expected to simultaneously provide optimized support for the diverse 5G use case categories. In this paper, we provide an overview of 5G research, standardization trials, and deployment challenges. Due to the enormous scope of 5G systems, it is necessary to provide some direction in a tutorial article, and in this overview, the focus is largely user centric, rather than device centric. In addition to surveying the state of play in the area, we identify leading technologies, evaluating their strengths and weaknesses, and outline the key challenges ahead, with research test beds delivering promising performance but pre-commercial trials lagging behind the desired 5G targets.
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paper_title: Crowd-Sourced Data Collection for Urban Monitoring via Mobile Sensors

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A considerable amount of research has addressed Internet of Things and connected communities. It is possible to exploit the sensing capabilities of connected communities, by leveraging the continuously growing use of cloud computing solutions and mobile devices. The pervasiveness of mobile sensors also enables the Mobile Crowd Sensing (MCS) paradigm, which aims at using mobile-embedded sensors to extend monitoring of multiple (environmental) phenomena in expansive urban areas. In this article, we discuss our approach with a cloud-based platform to pave the way for applying crowd sensing in urban scenarios. We have implemented a complete solution for environmental monitoring of several pollutants, like noise, air, electromagnetic fields, and so on in an urban area based on this paradigm. Through extensive experimentation, specifically on noise pollution, we show how the proposed infrastructure exhibits the ability to collect data from connected communities, and enables a seamless support of services needed for improving citizens’ quality of life and eventually helps city decision makers in urban planning.
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paper_title: Edge Computing Architecture for Mobile Crowdsensing

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Mobile crowdsensing (MCS) is a human-driven Internet of Things service empowering citizens to observe the phenomena of individual, community, or even societal value by sharing sensor data about their environment while on the move. Typical MCS service implementations utilize cloud-based centralized architectures, which consume a lot of computational resources and generate significant network traffic, both in mobile networks and toward cloud-based MCS services. Mobile edge computing (MEC) is a natural choice to distribute MCS solutions by moving computation to network edge, since an MEC-based architecture enables significant performance improvements due to the partitioning of problem space based on location, where real-time data processing and aggregation is performed close to data sources. This in turn reduces the associated traffic in mobile core and will facilitate MCS deployments of massive scale. This paper proposes an edge computing architecture adequate for massive scale MCS services by placing key MCS features within the reference MEC architecture. In addition to improved performance, the proposed architecture decreases privacy threats and permits citizens to control the flow of contributed sensor data. It is adequate for both data analytics and real-time MCS scenarios, in line with the 5G vision to integrate a huge number of devices and enable innovative applications requiring low network latency. Our analysis of service overhead introduced by distributed architecture and service reconfiguration at network edge performed on real user traces shows that this overhead is controllable and small compared with the aforementioned benefits. When enhanced by interoperability concepts, the proposed architecture creates an environment for the establishment of an MCS marketplace for bartering and trading of both raw sensor data and aggregated/processed information.
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paper_title: Does the Configuration of the Street Network Influence Where Outdoor Serious Violence Takes Place? Using Space Syntax to Test Crime Pattern Theory

paper_content:

Objectives ::: To examine the effect of the physical layout of the street network on the spatial distribution of outdoor serious violence. Crime pattern theory predicts crime would be more prevalent on more connected, accessible or traveled street segments, as these will be more likely to fall within an offender’s awareness space.
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paper_title: Fog computing and its role in the internet of things

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Fog Computing extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services. Defining characteristics of the Fog are: a) Low latency and location awareness; b) Wide-spread geographical distribution; c) Mobility; d) Very large number of nodes, e) Predominant role of wireless access, f) Strong presence of streaming and real time applications, g) Heterogeneity. In this paper we argue that the above characteristics make the Fog the appropriate platform for a number of critical Internet of Things (IoT) services and applications, namely, Connected Vehicle, Smart Grid, Smart Cities, and, in general, Wireless Sensors and Actuators Networks (WSANs).
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paper_title: Profiling Performance of Application Partitioning for Wearable Devices in Mobile Cloud and Fog Computing

paper_content:

Wearable devices have become essential in our daily activities. Due to battery constraints, the use of computing, communication, and storage resources is limited. Mobile cloud computing (MCC) and the recently emerged fog computing (FC) paradigms unleash unprecedented opportunities to augment the capabilities of wearable devices. Partitioning mobile applications and offloading computationally heavy tasks for execution to the cloud or edge of the network is the key. Offloading prolongs the lifetime of the batteries and allows wearable devices to gain access to the rich and powerful set of computing and storage resources of the cloud/edge. In this paper, we experimentally evaluate and discuss the rationale of application partitioning for MCC and FC. To experiment, we develop an Android-based application and benchmark energy and execution time performance of multiple partitioning scenarios. The results unveil architectural tradeoffs that exist between the paradigms and devise guidelines for proper power management of the service-centric Internet-of-Things applications.
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paper_title: Traffic Flow Prediction With Big Data: A Deep Learning Approach

paper_content:

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.
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paper_title: Internet of Things and Big Data Analytics for Smart and Connected Communities

paper_content:

This paper promotes the concept of smart and connected communities SCC, which is evolving from the concept of smart cities. SCC are envisioned to address synergistically the needs of remembering the past (preservation and revitalization), the needs of living in the present (livability), and the needs of planning for the future (attainability). Therefore, the vision of SCC is to improve livability, preservation, revitalization, and attainability of a community. The goal of building SCC for a community is to live in the present, plan for the future, and remember the past. We argue that Internet of Things (IoT) has the potential to provide a ubiquitous network of connected devices and smart sensors for SCC, and big data analytics has the potential to enable the move from IoT to real-time control desired for SCC. We highlight mobile crowdsensing and cyber-physical cloud computing as two most important IoT technologies in promoting SCC. As a case study, we present TreSight, which integrates IoT and big data analytics for smart tourism and sustainable cultural heritage in the city of Trento, Italy.
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paper_title: Deep Learning in Mobile and Wireless Networking: A Survey

paper_content:

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, real-time extraction of fine-grained analytics, and agile management of network resources, so as to maximize user experience. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques, in order to help manage the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper, we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.
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paper_title: Deep Learning the City : Quantifying Urban Perception At A Global Scale

paper_content:

Computer vision methods that quantify the perception of urban environment are increasingly being used to study the relationship between a city’s physical appearance and the behavior and health of its residents. Yet, the throughput of current methods is too limited to quantify the perception of cities across the world. To tackle this challenge, we introduce a new crowdsourced dataset containing 110,988 images from 56 cities, and 1,170,000 pairwise comparisons provided by 81,630 online volunteers along six perceptual attributes: safe, lively, boring, wealthy, depressing, and beautiful. Using this data, we train a Siamese-like convolutional neural architecture, which learns from a joint classification and ranking loss, to predict human judgments of pairwise image comparisons. Our results show that crowdsourcing combined with neural networks can produce urban perception data at the global scale.
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paper_title: Advances in Crowd Analysis for Urban Applications Through Urban Event Detection

paper_content:

The recent expansion of pervasive computing technology has contributed with novel means to pursue human activities in urban space. The urban dynamics unveiled by these means generate an enormous amount of data. These data are mainly endowed by portable and radio-frequency devices, transportation systems, video surveillance, satellites, unmanned aerial vehicles, and social networking services. This has opened a new avenue of opportunities, to understand and predict urban dynamics in detail, and plan various real-time services and applications in response to that. Over the last decade, certain aspects of the crowd, e.g., mobility, sentimental, size estimation and behavioral, have been analyzed in detail and the outcomes have been reported. This paper mainly conducted an extensive survey on various data sources used for different urban applications, the state-of-the-art on urban data generation techniques and associated processing methods in order to demonstrate their merits and capabilities. Then, available open-access crowd data sets for urban event detection are provided along with relevant application programming interfaces. In addition, an outlook on a support system for urban application is provided which fuses data from all the available pervasive technology sources and finally, some open challenges and promising research directions are outlined.
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paper_title: Learning-Assisted Optimization in Mobile Crowd Sensing: A Survey

paper_content:

Mobile crowd sensing (MCS) is a relatively new paradigm for collecting real-time and location-dependent urban sensing data. Given its applications, it is crucial to optimize the MCS process with the objective of maximizing the sensing quality and minimizing the sensing cost. While earlier studies mainly tackle this issue by designing different combinatorial optimization algorithms, there is a new trend to further optimize MCS by integrating learning techniques to extract knowledge, such as participants’ behavioral patterns or sensing data correlation. In this paper, we perform an extensive literature review of learning-assisted optimization approaches in MCS. Specifically, from the perspective of the participant and the task, we organize the existing work into a conceptual framework, present different learning and optimization methods, and describe their evaluation. Furthermore, we discuss how different techniques can be combined to form a complete solution. In the end, we point out existing limitations, which can inform and guide future research directions.
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paper_title: Global change and the ecology of cities.

paper_content:

Urban areas are hot spots that drive environmental change at multiple scales. Material demands of production and human consumption alter land use and cover, biodiversity, and hydrosystems locally to regionally, and urban waste discharge affects local to global biogeochemical cycles and climate. For urbanites, however, global environmental changes are swamped by dramatic changes in the local environment. Urban ecology integrates natural and social sciences to study these radically altered local environments and their regional and global effects. Cities themselves present both the problems and solutions to sustainability challenges of an increasingly urbanized world.
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paper_title: On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

paper_content:

Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network. MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks. This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies. It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options. In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties. Finally, this paper overviews the current standardization activities and elaborates further on open research challenges.
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paper_title: Deep learning

paper_content:

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
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paper_title: Smart Cities in Europe

paper_content:

Urban performance currently depends not only on a city's endowment of hard infrastructure (physical capital), but also, and increasingly so, on the availability and quality of knowledge communication and social infrastructure (human and social capital). The latter form of capital is decisive for urban competitiveness. Against this background, the concept of the “smart city” has recently been introduced as a strategic device to encompass modern urban production factors in a common framework and, in particular, to highlight the importance of Information and Communication Technologies (ICTs) in the last 20 years for enhancing the competitive profile of a city. The present paper aims to shed light on the often elusive definition of the concept of the “smart city.” We provide a focused and operational definition of this construct and present consistent evidence on the geography of smart cities in the EU27. Our statistical and graphical analyses exploit in depth, for the first time to our knowledge, the most re...
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paper_title: Mobile Edge Computing, Fog et al.: A Survey and Analysis of Security Threats and Challenges

paper_content:

Abstract For various reasons, the cloud computing paradigm is unable to meet certain requirements (e.g. low latency and jitter, context awareness, mobility support) that are crucial for several applications (e.g. vehicular networks, augmented reality). To fulfill these requirements, various paradigms, such as fog computing, mobile edge computing, and mobile cloud computing, have emerged in recent years. While these edge paradigms share several features, most of the existing research is compartmentalized; no synergies have been explored. This is especially true in the field of security, where most analyses focus only on one edge paradigm, while ignoring the others. The main goal of this study is to holistically analyze the security threats, challenges, and mechanisms inherent in all edge paradigms, while highlighting potential synergies and venues of collaboration. In our results, we will show that all edge paradigms should consider the advances in other paradigms.
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paper_title: SPACE-TA: Cost-Effective Task Allocation Exploiting Intradata and Interdata Correlations in Sparse Crowdsensing

paper_content:

Data quality and budget are two primary concerns in urban-scale mobile crowdsensing. Traditional research on mobile crowdsensing mainly takes sensing coverage ratio as the data quality metric rather than the overall sensed data error in the target-sensing area. In this article, we propose to leverage spatiotemporal correlations among the sensed data in the target-sensing area to significantly reduce the number of sensing task assignments. In particular, we exploit both intradata correlations within the same type of sensed data and interdata correlations among different types of sensed data in the sensing task. We propose a novel crowdsensing task allocation framework called SPACE-TA (SPArse Cost-Effective Task Allocation), combining compressive sensing, statistical analysis, active learning, and transfer learning, to dynamically select a small set of subareas for sensing in each timeslot (cycle), while inferring the data of unsensed subareas under a probabilistic data quality guarantee. Evaluations on real-life temperature, humidity, air quality, and traffic monitoring datasets verify the effectiveness of SPACE-TA. In the temperature-monitoring task leveraging intradata correlations, SPACE-TA requires data from only 15.5% of the subareas while keeping the inference error below 0.25°C in 95% of the cycles, reducing the number of sensed subareas by 18.0% to 26.5% compared to baselines. When multiple tasks run simultaneously, for example, for temperature and humidity monitoring, SPACE-TA can further reduce ∼10% of the sensed subareas by exploiting interdata correlations.
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paper_title: EdgeSense: Edge-Mediated Spatial- Temporal Crowdsensing

paper_content:

Edge computing recently is increasingly popular due to the growth of data size and the need of sensing with the reduced center. Based on Edge computing architecture, we propose a novel crowdsensing framework called <italic>Edge-Mediated Spatial-Temporal Crowdsensing</italic>. This algorithm targets on receiving the environment information such as air pollution, temperature, and traffic flow in some parts of the goal area, and does not aggregate sensor data with its location information. Specifically, <italic>EdgeSense</italic> works on top of a secured peer-to-peer network consisted of participants and propose a novel <italic>Decentralized Spatial-Temporal Crowdsensing</italic> framework based on <italic>Parallelized Stochastic Gradient Descent</italic>. To approximate the sensing data in each part of the target area in each sensing cycle, <italic>EdgeSense</italic> uses the local sensor data in participants’ mobile devices to learn the low-rank characteristic and then recovers the sensing data from it. We evaluate the <italic>EdgeSense</italic> on the real-world data sets (temperature <xref ref-type="bibr" rid="ref1">[1]</xref> and PM2.5 <xref ref-type="bibr" rid="ref2">[2]</xref> data sets), where our algorithm can achieve low error in approximation and also can compete with the baseline algorithm which is designed using centralized and aggregated mechanism.
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paper_title: Towards Personalized Task Matching in Mobile Crowdsensing via Fine-Grained User Profiling

paper_content:

In mobile crowdsensing, finding the best match between tasks and users is crucial to ensure both the quality and effectiveness of a crowdsensing system. Existing works usually assume a centralized task assignment by the platform, without addressing the need of fine-grained personalized task matching. In this paper, we argue that it is essential to match tasks to users based on a careful characterization of both the users' preferences and reliability levels. To that end, we propose a personalized task recommender system for mobile crowdsensing, which recommends tasks to users based on a recommendation score that jointly takes each user's preference and reliability into consideration. We first present a simple but effective method to profile the users' preferences by exploiting the implicit feedback from their historical performance. Then, to profile the users' reliability levels, we formalize the problem as a semi-supervised learning model, and propose an efficient block coordinate descent algorithm to solve the problem. For some tasks that lack historical information, we further propose a matrix factorization method to infer the users' reliability on those tasks. We conduct extensive experiments to evaluate the performance of our system, and the evaluation results demonstrate that our system can achieve superior performance to our benchmarks in both user profiling and personalized task matching.
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paper_title: Robust Mobile Crowd Sensing: When Deep Learning Meets Edge Computing

paper_content:

The emergence of MCS technologies provides a cost-efficient solution to accommodate large-scale sensing tasks. However, despite the potential benefits of MCS, there are several critical issues that remain to be solved, such as lack of incentive-compatible mechanisms for recruiting participants, lack of data validation, and high traffic load and latency. This motivates us to develop robust mobile crowd sensing (RMCS), a framework that integrates deep learning based data validation and edge computing based local processing. First, we present a comprehensive state-of-the-art literature review. Then, the conceptual design architecture of RMCS and practical implementations are described in detail. Next, a case study of smart transportation is provided to demonstrate the feasibility of the proposed RMCS framework. Finally, we identify several open issues and conclude the article.
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Title: A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Section 1: Introduction
Description 1: Introduce the concept of Mobile Crowdsensing Systems (MCS), its relevance, background statistics, and the motivation driving research in this area.

Section 2: Background
Description 2: Explore the motivations giving rise to MCS, a history of related surveys, a timeline of milestone works, key technological factors, and acronyms and terminologies used in MCS.

Section 3: MCS in a Nutshell
Description 3: Present MCS as a layered architecture, discuss its main data collection frameworks (DCFs), theoretical works on operational research and optimization problems, platforms, simulators, datasets, and MCS as a business paradigm with final remarks.

Section 4: Taxonomies and Classification on Application Layer
Description 4: Analyze and classify the application layer of MCS systems, detailing the task and user-related taxonomies and relevant research work classifications.

Section 5: Taxonomies and Classification on Data Layer
Description 5: Discuss the taxonomies related to the data layer, covering management and processing aspects, and classify related works based on these taxonomies.

Section 6: Taxonomies and Classification on Communication Layer
Description 6: Provide taxonomies and classifications for the communication layer, addressing technologies and reporting methodologies used in MCS systems.

Section 7: Taxonomies and Classification on Sensing Layer
Description 7: Discuss the taxonomies related to the sensing layer, focusing on elements and the sampling process, including the classification of existing literature based on these taxonomies.

Section 8: Discussion
Description 8: Provide a retrospective analysis and lessons learned from the past decade of MCS research. Highlight upcoming challenges, application scenarios, and inter-disciplinary interconnections with other research areas.

Section 9: Concluding Remarks
Description 9: Summarize the key points of the survey, emphasize the novelty, categorize the comprehensive architectural taxonomy, and discuss future research directions based on the survey findings.