A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization
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paper_title: Opportunistic sensing: Security challenges for the new paradigm

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We study the security challenges that arise in opportunistic people-centric sensing, a new sensing paradigm leveraging humans as part of the sensing infrastructure. Most prior sensor-network research has focused on collecting and processing environmental data using a static topology and an application-aware infrastructure, whereas opportunistic sensing involves collecting, storing, processing and fusing large volumes of data related to everyday human activities. This highly dynamic and mobile setting, where humans are the central focus, presents new challenges for information security, because data originates from sensors carried by people— not tiny sensors thrown in the forest or attached to animals. In this paper we aim to instigate discussion of this critical issue, because opportunistic people-centric sensing will never succeed without adequate provisions for security and privacy. To that end, we outline several important challenges and suggest general solutions that hold promise in this new sensing paradigm.
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paper_title: Challenge: ubiquitous location-aware computing and the "place lab" initiative

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To be widely adopted, location-aware computing must be as effortless, familiar and rewarding as web search tools like Google. We envisage the global scale Place Lab, consisting of an open software base and a community building activity as a way to bootstrap the broad adoption of location-aware computing. The initiative is a laboratory because it will also be a vehicle for research and instruction, especially in the formative stages. The authors draw on their experiences with campus and building-scale location systems to identify the technological and social barriers to a truly ubiquitous deployment. With a grasp of these "barriers to adoption," we present a usage scenario, the problems in realizing this scenario, and how these problems will be addressed. We conclude with a sketch of the multi-organization cooperative being formed to move this effort forward.
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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: The case for crowd computing

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We introduce and motivate "crowd computing", which combines mobile devices and social interactions to achieve large-scale distributed computation. An opportunistic network of mobile devices offers substantial aggregate bandwidth and processing power. In this paper, we analyse encounter traces to place an upper bound on the amount of computation that is possible in such networks. We also investigate a practical task-farming algorithm that approaches this upper bound, and show that exploiting social structure can dramatically increase its performance.
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paper_title: reCAPTCHA: Human-Based Character Recognition via Web Security Measures

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CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart) are widespread security measures on the World Wide Web that prevent automated programs from abusing online services. They do so by asking humans to perform a task that computers cannot yet perform, such as deciphering distorted characters. Our research explored whether such human effort can be channeled into a useful purpose: helping to digitize old printed material by asking users to decipher scanned words from books that computerized optical character recognition failed to recognize. We showed that this method can transcribe text with a word accuracy exceeding 99%, matching the guarantee of professional human transcribers. Our apparatus is deployed in more than 40,000 Web sites and has transcribed over 440 million words.
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paper_title: Social sensing for epidemiological behavior change

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An important question in behavioral epidemiology and public health is to understand how individual behavior is affected by illness and stress. Although changes in individual behavior are intertwined with contagion, epidemiologists today do not have sensing or modeling tools to quantitatively measure its effects in real-world conditions. In this paper, we propose a novel application of ubiquitous computing. We use mobile phone based co-location and communication sensing to measure characteristic behavior changes in symptomatic individuals, reflected in their total communication, interactions with respect to time of day (e.g., late night, early morning), diversity and entropy of face-to-face interactions and movement. Using these extracted mobile features, it is possible to predict the health status of an individual, without having actual health measurements from the subject. Finally, we estimate the temporal information flux and implied causality between physical symptoms, behavior and mental health.
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paper_title: Motion Recognition Assisted Indoor Wireless Navigation on a Mobile Phone

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The paper presents an indoor navigation solution combining physical motion recognition with WLAN positioning on a smart phone. Orientationindependent features are extracted from vertical and horizontal components of acceleration. The simple features such as the mean of horizontal acceleration, the variance of acceleration magnitude, and the variance of horizontal acceleration are selected as the nodes in a decision tree. Six common motion modes during indoor navigation, e.g., static, standing with hand swinging, normal walking with holding the phone in hand, normal walking with hand swinging, fast walking, and U-turning are detected. A fingerprinting based WLAN positioning approach offers the headings and initial positions for pedestrian dead reckoning with about 10 seconds interval. The velocities for six motion modes are trained by five testers and applied in the dead reckoning during the WLAN positioning gap. Test results indicate that the motion mode is recognized correctly in 95% of test cases. The field test shows 4.6m horizontal mean error for motion recognition assisted indoor wireless positioning.
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paper_title: Indoor Positioning and Navigation with Camera Phones

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This low-cost indoor navigation system runs on off-the-shelf camera phones. More than 2,000 users at four different large-scale events have already used it. The system uses built-in cameras to determine user location in real time by detecting unobtrusive fiduciary markers. The required infrastructure is limited to paper markers and static digital maps, and common devices are used, facilitating quick deployment in new environments. The authors have studied the application quantitatively in a controlled environment and qualitatively during deployment at four large international events. According to test users, marker-based navigation is easier to use than conventional mobile digital maps. Moreover, the users' location awareness in navigation tasks improved. Experiences drawn from questionnaires, usage log data, and user interviews further highlight the benefits of this approach.
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paper_title: iParking: An Intelligent Indoor Location-Based Smartphone Parking Service

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Indoor positioning technologies have been widely studied with a number of solutions being proposed, yet substantial applications and services are still fairly primitive. Taking advantage of the emerging concept of the connected car, the popularity of smartphones and mobile Internet, and precise indoor locations, this study presents the development of a novel intelligent parking service called iParking. With the iParking service, multiple parties such as users, parking facilities and service providers are connected through Internet in a distributed architecture. The client software is a light-weight application running on a smartphone, and it works essentially based on a precise indoor positioning solution, which fuses Wireless Local Area Network (WLAN) signals and the measurements of the built-in sensors of the smartphones. The positioning accuracy, availability and reliability of the proposed positioning solution are adequate for facilitating the novel parking service. An iParking prototype has been developed and demonstrated in a real parking environment at a shopping mall. The demonstration showed how the iParking service could improve the parking experience and increase the efficiency of parking facilities. The iParking is a novel service in terms of cost- and energy-efficient solution.
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paper_title: Participatory Sensing: Crowdsourcing Data from Mobile Smartphones in Urban Spaces

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The recent wave of sensor-rich, Internet-enabled, smart mobile devices such as the Apple iPhone has opened the door for a novel paradigm for monitoring the urban landscape known as participatory sensing. Using this paradigm, ordinary citizens can collect multi-modal data streams from the surrounding environment using their mobile devices and share the same using existing communication infrastructure (e.g., 3G service or WiFi access points). The data contributed from multiple participants can be combined to build a spatiotemporal view of the phenomenon of interest and also to extract important community statistics. Given the ubiquity of mobile phones and the high density of people in metropolitan areas, participatory sensing can achieve an unprecedented level of coverage in both space and time for observing events of interest in urban spaces. Several exciting participatory sensing applications have emerged in recent years. For example, GPS traces uploaded by drivers and passengers can be used to generate real time traffic statistics. Similarly, street-level audio samples collected by pedestrians can be aggregated to create a citywide noise map. In this advanced seminar, we will provide a comprehensive overview of this new and exciting paradigm and outline the major research challenges.
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paper_title: Using LS-SVM Based Motion Recognition for Smartphone Indoor Wireless Positioning

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The paper presents an indoor navigation solution by combining physical motion recognition with wireless positioning. Twenty-seven simple features are extracted from the built-in accelerometers and magnetometers in a smartphone. Eight common motion states used during indoor navigation are detected by a Least Square-Support Vector Machines (LS-SVM) classification algorithm, e.g., static, standing with hand swinging, normal walking while holding the phone in hand, normal walking with hand swinging, fast walking, U-turning, going up stairs, and going down stairs. The results indicate that the motion states are recognized with an accuracy of up to 95.53% for the test cases employed in this study. A motion recognition assisted wireless positioning approach is applied to determine the position of a mobile user. Field tests show a 1.22 m mean error in “Static Tests” and a 3.53 m in “Stop-Go Tests”.
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paper_title: Human computation: a survey and taxonomy of a growing field

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The rapid growth of human computation within research and industry has produced many novel ideas aimed at organizing web users to do great things. However, the growth is not adequately supported by a framework with which to understand each new system in the context of the old. We classify human computation systems to help identify parallels between different systems and reveal "holes" in the existing work as opportunities for new research. Since human computation is often confused with "crowdsourcing" and other terms, we explore the position of human computation with respect to these related topics.
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paper_title: RADAR: an in-building RF-based user location and tracking system

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The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.
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paper_title: Visual-aided Two-dimensional Pedestrian Indoor Navigation with a Smartphone

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Indoor pedestrian positioning sets severe challenges for a navigation system. To be applicable for pedestrian navigation the platform used has to be small in size and reasonably priced. Smartphones fulfill these requirements satisfyingly. GNSS signals are degraded indoors and in order to obtain accurate navigation aiding from other sensors is needed. Self-contained sensors provide valuable information about the motion of the pedestrian and when integrated with GNSS measurements a position solution is typically obtainable indoors. The accuracy is however decreased due to errors in the measurements of the self-contained sensors introduced by various environmental disturbances. When the effect of the disturbance is constrained using visualaiding the accuracy can be increased to an acceptable level. This paper introduces a visual-aided twodimensional indoor pedestrian navigation system integrating measurements from GNSS, Bluetooth, WLAN, self-contained sensors, and heading change information obtained from consecutive images. The integration is performed with an Extended Kalman filter. Reliability information of the heading change measurements calculated from images using vanishing points is provided to the filter and utilized in the integration. The visual-aiding algorithm is computationally lightweight taking into account the restricted resources of the smartphone. In the conducted experiment, the accuracy of the position solution is increased by 1.2 meters due to the visual-aiding.
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paper_title: Efficient WiFi fingerprint training using semi-supervised learning

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Fingerfrinting based WiFi positioning approach needs an off-line training phase to build a radio map with received signal strength indication vector of each reference point. In existing systems, this training phase may cost a tremendous amount of workload to achieve satisfying location result. To cut down on the workload notably and guarantee the location result in the meantime, we will introduce an efficient WiFi fingerprint training method: Fa-Fi namely fast fingerprint generation, which uses semi-supervised learning in this article. This proposed method can reduce the training phase time cost to about 1/5, and guarantee the localization accuracy at the same time.
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paper_title: A Probabilistic Approach to WLAN User Location Estimation

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We estimate the location of a WLAN user based on radio signal strength measurements performed by the user's mobile terminal. In our approach the physical properties of the signal propagation are not taken into account directly. Instead the location estimation is regarded as a machine learning problem in which the task is to model how the signal strengths are distributed in different geographical areas based on a sample of measurements collected at several known locations. We present a probabilistic framework for solving the location estimation problem. In the empirical part of the paper we demonstrate the feasibility of this approach by reporting results of field tests in which a probabilistic location estimation method is validated in a real-world indoor environment.
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paper_title: A wireless LAN-based indoor positioning technology

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Context-aware computing is an emerging computing paradigm that can provide new or improved services by exploiting user context information. In this paper, we present a wireless-local-area-network-based (WLAN-based) indoor positioning technology. The wireless device deploys a position-determination model to gather location information from collected WLAN signals. A model-based signal distribution training scheme is proposed to trade off the accuracy of signal distribution and training workload. A tracking-assistant positioning algorithm is presented to employ knowledge of the area topology to assist the procedure of position determination. We have set up a positioning system at the IBM China Research Laboratory. Our experimental results indicate an accuracy of 2 m with a 90% probability for static devices and, for moving (walking) devices, an accuracy of 5 m with a 90% probability. Moreover, the complexity of the training procedure is greatly reduced compared with other positioning algorithms.
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paper_title: WLAN location determination via clustering and probability distributions

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We present a WLAN location determination technique, the Joint Clustering technique, that uses: (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy.
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paper_title: RADAR: an in-building RF-based user location and tracking system

paper_content:

The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.
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paper_title: The uses of ambient light for ubiquitous positioning

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This paper proposed ambient light (ambilight) as a new type of signal sources for positioning. The possibility and methods of ambilight positioning were presented in this paper. It has been shown that two kinds of observables of ambient light can be used for positioning through different principles. Ambilight intensity spectrum measurements have highly location dependency, and they can be used for positioning with the traditional fingerprinting approach. Total ambilight irradiance intensity is used to detect the proximity of a lighting source, and a location solution can be further resolved with the support of knowledge of lighting infrastructure. Ambilight positioning can work in areas where other traditional techniques are not able to function. An ambilight sensor is cost-efficient and miniature in size, and it can be easily integrated with other sensors to form a hybrid positioning system. This paper was concluded with discussions on the possibility, applicability, challenges and outlook of the new ambient light positioning techniques.
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paper_title: SurroundSense: mobile phone localization via ambience fingerprinting

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A growing number of mobile computing applications are centered around the user's location. The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, there have been few attempts in recognizing logical locations. This paper argues that the increasing number of sensors on mobile phones presents new opportunities for logical localization. We postulate that ambient sound, light, and color in a place convey a photo-acoustic signature that can be sensed by the phone's camera and microphone. In-built accelerometers in some phones may also be useful in inferring broad classes of user-motion, often dictated by the nature of the place. By combining these optical, acoustic, and motion attributes, it may be feasible to construct an identifiable fingerprint for logical localization. Hence, users in adjacent stores can be separated logically, even when their physical positions are extremely close. We propose SurroundSense, a mobile phone based system that explores logical localization via ambience fingerprinting. Evaluation results from 51 different stores show that SurroundSense can achieve an average accuracy of 87% when all sensing modalities are employed. We believe this is an encouraging result, opening new possibilities in indoor localization.
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paper_title: WiFi-SLAM Using Gaussian Process Latent Variable Models

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WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.
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paper_title: FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem

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The ability to simultaneously localize a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. However, few approaches to this problem scale up to handle the very large number of landmarks present in real environments. Kalman filter-based algorithms, for example, require time quadratic in the number of landmarks to incorporate each sensor observation. This paper presents FastSLAM, an algorithm that recursively estimates the full posterior distribution over robot pose and landmark locations, yet scales logarithmically with the number of landmarks in the map. This algorithm is based on an exact factorization of the posterior into a product of conditional landmark distributions and a distribution over robot paths. The algorithm has been run successfully on as many as 50,000 landmarks, environments far beyond the reach of previous approaches. Experimental results demonstrate the advantages and limitations of the FastSLAM algorithm on both simulated and real-world data.
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paper_title: 1 A Tutorial on Graph-Based SLAM

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Being able to build a map of the environment and to simultaneously localize within this map is an essential skill for mobile robots navigating in unknown environments in absence of external referencing systems such as GPS. This so-called simultaneous localization and mapping (SLAM) problem has been one of the most popular research topics in mobile robotics for the last two decades and efficient approaches for solving this task have been proposed. One intuitive way of formulating SLAM is to use a graph whose nodes correspond to the poses of the robot at different points in time and whose edges represent constraints between the poses. The latter are obtained from observations of the environment or from movement actions carried out by the robot. Once such a graph is constructed, the map can be computed by finding the spatial configuration of the nodes that is mostly consistent with the measurements modeled by the edges. In this paper, we provide an introductory description to the graph-based SLAM problem. Furthermore, we discuss a state-of-the-art solution that is based on least-squares error minimization and exploits the structure of the SLAM problems during optimization. The goal of this tutorial is to enable the reader to implement the proposed methods from scratch.
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paper_title: Simultaneous Localization and Mapping for pedestrians using distortions of the local magnetic field intensity in large indoor environments

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We present a Simultaneous Localization and Mapping (SLAM) algorithm based on measurements of the ambient magnetic field strength (MagSLAM) that allows quasi-real-time mapping and localization in buildings, where pedestrians with foot-mounted sensors are the subjects to be localized. We assume two components to be present: firstly a source of odometry (human step measurements), and secondly a sensor of the local magnetic field intensity. Our implementation follows the FastSLAM factorization using a particle filter. We augment the hexagonal transition map used in the pre-existing FootSLAM algorithm with local maps of the magnetic field strength, binned in a hierarchical hexagonal structure. We performed extensive experiments in a number of different buildings and present the results for five data sets for which we have ground truth location information. We consider the results obtained using MagSLAM to be strong evidence that scalable and accurate localization is possible without an a priori map.
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paper_title: Growing an organic indoor location system

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Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments.
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paper_title: Crowdsourcing with Smartphones

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Smartphones can reveal crowdsourcing's full potential and let users transparently contribute to complex and novel problem solving. This emerging area is illustrated through a taxonomy that classifies the mobile crowdsourcing field and through three new applications that optimize location-based search and similarity services based on crowd-generated data. Such applications can be deployed on SmartLab, a cloud of more than 40 Android devices deployed at the University of Cyprus that provides an open testbed to facilitate research and development of smartphone applications on a massive scale.
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paper_title: Growing an organic indoor location system

paper_content:

Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments.
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paper_title: Combining human and machine intelligence in large-scale crowdsourcing

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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: Quality Control in Crowdsourcing Systems: Issues and Directions

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As a new distributed computing model, crowdsourcing lets people leverage the crowd's intelligence and wisdom toward solving problems. This article proposes a framework for characterizing various dimensions of quality control in crowdsourcing systems, a critical issue. The authors briefly review existing quality-control approaches, identify open issues, and look to future research directions. In the Web extra, the authors discuss both design-time and runtime approaches in more detail.
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paper_title: Covariance consistency methods for fault-tolerant distributed data fusion

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Abstract This paper presents a general, rigorous, and fault-tolerant framework for maintaining consistent mean and covariance estimates in an arbitrary, dynamic, distributed network of information processing nodes. In particular, a solution is provided that addresses the information deconfliction problem that arises when estimates from two or more different nodes are determined to be inconsistent with each other, e.g., when two high precision (small covariance) estimates place the position of a particular object at very different locations. The challenge is to be able to resolve such inconsistencies without having to access and exploit global information to determine which of the estimates is spurious. The solution proposed in this paper is called Covariance Union.
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paper_title: Crowd IQ: aggregating opinions to boost performance

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We show how the quality of decisions based on the aggregated opinions of the crowd can be conveniently studied using a sample of individual responses to a standard IQ questionnaire. We aggregated the responses to the IQ questionnaire using simple majority voting and a machine learning approach based on a probabilistic graphical model. The score for the aggregated questionnaire, Crowd IQ, serves as a quality measure of decisions based on aggregating opinions, which also allows quantifying individual and crowd performance on the same scale. ::: ::: We show that Crowd IQ grows quickly with the size of the crowd but saturates, and that for small homogeneous crowds the Crowd IQ significantly exceeds the IQ of even their most intelligent member. We investigate alternative ways of aggregating the responses and the impact of the aggregation method on the resulting Crowd IQ. We also discuss Contextual IQ, a method of quantifying the individual participant's contribution to the Crowd IQ based on the Shapley value from cooperative game theory.
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paper_title: Smartphone-Based Collaborative and Autonomous Radio Fingerprinting

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Although active research has recently been conducted on received signal strength (RSS) fingerprint-based indoor localization, most of the current systems hardly overcome the costly and time-consuming offline training phase. In this paper, we propose an autonomous and collaborative RSS fingerprint collection and localization system. Mobile users track their position with inertial sensors and measure RSS from the surrounding access points. In this scenario, anonymous mobile users automatically collect data in daily life without purposefully surveying an entire building. The server progressively builds up a precise radio map as more users interact with their fingerprint data. The time drift error of inertial sensors is also compromised at run-time with the fingerprint-based localization, which runs with the collective fingerprints being currently built by the server. The proposed system has been implemented on a recent Android smartphone. The experiment results show that reasonable location accuracy is obtained with automatic fingerprinting in indoor environments.
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paper_title: LOF: identifying density-based local outliers

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For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that the degree depends on how isolated the object is with respect to the surrounding neighborhood. We give a detailed formal analysis showing that LOF enjoys many desirable properties. Using real-world datasets, we demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms we show that our approach of finding local outliers can be practical.
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paper_title: Learning From Crowds

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For many supervised learning tasks it may be infeasible (or very expensive) to obtain objective and reliable labels. Instead, we can collect subjective (possibly noisy) labels from multiple experts or annotators. In practice, there is a substantial amount of disagreement among the annotators, and hence it is of great practical interest to address conventional supervised learning problems in this scenario. In this paper we describe a probabilistic approach for supervised learning when we have multiple annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method is superior to the commonly used majority voting baseline.
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paper_title: Database updating through user feedback in fingerprint-based Wi-Fi location systems

paper_content:

Wi-Fi fingerprinting is a technique which can provide location in GPS-denied environments, relying exclusively on Wi-Fi signals. It first requires the construction of a database of “fingerprints”, i.e. signal strengths from different access points (APs) at different reference points in the desired coverage area. The location of the device is then obtained by measuring the signal strengths at its location, and comparing it with the different reference fingerprints in the database. The main disadvantage of this technique is the labour required to build and maintain the fingerprints database, which has to be rebuilt every time a significant change in the wireless environment occurs, such as installation or removal of new APs, changes in the layout of a building, etc. This paper investigates a new method to utilise user feedback as a way of monitoring changes in the wireless environment. It is based on a system of “points” given to each AP in the database. When an AP is switched off, the number of points associated with that AP will gradually reduce as the users give feedback, until it is eventually deleted from the database. If a new AP is installed, the system will detect it and update the database with new fingerprints. Our proposed system has two main advantages. First it can be used as a tool to monitor the wireless environment in a given place, detecting faulty APs or unauthorised installation of new ones. Second, it regulates the size of the database, unlike other systems where feedback is only used to insert new fingerprints in the database.
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paper_title: The multidimensional wisdom of crowds

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Distributing labeling tasks among hundreds or thousands of annotators is an increasingly important method for annotating large datasets. We present a method for estimating the underlying value (e.g. the class) of each image from (noisy) annotations provided by multiple annotators. Our method is based on a model of the image formation and annotation process. Each image has different characteristics that are represented in an abstract Euclidean space. Each annotator is modeled as a multidimensional entity with variables representing competence, expertise and bias. This allows the model to discover and represent groups of annotators that have different sets of skills and knowledge, as well as groups of images that differ qualitatively. We find that our model predicts ground truth labels on both synthetic and real data more accurately than state of the art methods. Experiments also show that our model, starting from a set of binary labels, may discover rich information, such as different "schools of thought" amongst the annotators, and can group together images belonging to separate categories.
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paper_title: Trust-based fusion of untrustworthy information in crowdsourcing applications

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In this paper, we address the problem of fusing untrustworthy reports provided from a crowd of observers, while simultaneously learning the trustworthiness of individuals. To achieve this, we construct a likelihood model of the users's trustworthiness by scaling the uncertainty of its multiple estimates with trustworthiness parameters. We incorporate our trust model into a fusion method that merges estimates based on the trust parameters and we provide an inference algorithm that jointly computes the fused output and the individual trustworthiness of the users based on the maximum likelihood framework. We apply our algorithm to cell tower local- isation using real-world data from the OpenSignal project and we show that it outperforms the state-of-the-art methods in both accuracy, by up to 21%, and consistency, by up to 50% of its predictions.
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paper_title: Spatial Weighted Outlier Detection

paper_content:

Spatial outliers are the spatial objects with distinct features from their surrounding neighbors. Detection of spatial outliers helps reveal valuable information from large spatial data sets. In many real applications, spatial objects can not be simply abstracted as isolated points. They have different boundary, size, volume, and location. These spatial properties affect the impact of a spatial object on its neighbors and should be taken into consideration. In this paper, we propose two spatial outlier detection methods which integrate the impact of spatial properties to the outlierness measurement. Experimental results on a real data set demonstrate the effectiveness of the proposed algorithms.
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paper_title: Whose vote should count more: Optimal integration of labels from labelers of unknown expertise

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Modern machine learning-based approaches to computer vision require very large databases of hand labeled images. Some contemporary vision systems already require on the order of millions of images for training (e.g., Omron face detector [9]). New Internet-based services allow for a large number of labelers to collaborate around the world at very low cost. However, using these services brings interesting theoretical and practical challenges: (1) The labelers may have wide ranging levels of expertise which are unknown a priori, and in some cases may be adversarial; (2) images may vary in their level of difficulty; and (3) multiple labels for the same image must be combined to provide an estimate of the actual label of the image. Probabilistic approaches provide a principled way to approach these problems. In this paper we present a probabilistic model and use it to simultaneously infer the label of each image, the expertise of each labeler, and the difficulty of each image. On both simulated and real data, we demonstrate that the model outperforms the commonly used "Majority Vote" heuristic for inferring image labels, and is robust to both noisy and adversarial labelers.
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paper_title: WLAN location determination via clustering and probability distributions

paper_content:

We present a WLAN location determination technique, the Joint Clustering technique, that uses: (1) signal strength probability distributions to address the noisy wireless channel, and (2) clustering of locations to reduce the computational cost of searching the radio map. The Joint Clustering technique reduces computational cost by more than an order of magnitude, compared to the current state of the art techniques, allowing non-centralized implementation on mobile clients. Results from 802.11-equipped iPAQ implementations show that the new technique gives user location to within 7 feet with over 90% accuracy.
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paper_title: RADAR: an in-building RF-based user location and tracking system

paper_content:

The proliferation of mobile computing devices and local-area wireless networks has fostered a growing interest in location-aware systems and services. In this paper we present RADAR, a radio-frequency (RF)-based system for locating and tracking users inside buildings. RADAR operates by recording and processing signal strength information at multiple base stations positioned to provide overlapping coverage in the area of interest. It combines empirical measurements with signal propagation modeling to determine user location and thereby enable location-aware services and applications. We present experimental results that demonstrate the ability of RADAR to estimate user location with a high degree of accuracy.
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paper_title: Prediction of Protein Structural Classes

paper_content:

A protein is usually classified into one of the following five struc- tural classes: a!, j3, a! +j3, a!/j3, and ( (irregular). The structural class of aprotein is correlated with its amino acid composition. However, given the amino acid composition of aprotein, how may one predict its structural class? Various efforts have been made in addressing this problem. This review addresses the progress in this field, with the focus on the state of the art, which is featured by a novel prediction algorithm and a recently developed database. The novel algorithm is characterized by a covariance matrix that takes into account the coupling effect among different amino acid components of a protein. The new database was established based on the requirement that the classes should have (1) as many nonhomologous structures as possible, (2) good quality structure, and (3) typical or distinguishable features for each of the structural classes concerned. The very high success rate for both the training-set proteins and the testing-set proteins, which has been further validated by a simulated analysis and a jackknife analysis, indicates that it is possible to predict the structural class of a protein according to its amino acid composition if an ideal and complete database can be established. It also suggests that the overall fold of a protein is basically determined by its amino acid composition.
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paper_title: Challenge: ubiquitous location-aware computing and the "place lab" initiative

paper_content:

To be widely adopted, location-aware computing must be as effortless, familiar and rewarding as web search tools like Google. We envisage the global scale Place Lab, consisting of an open software base and a community building activity as a way to bootstrap the broad adoption of location-aware computing. The initiative is a laboratory because it will also be a vehicle for research and instruction, especially in the formative stages. The authors draw on their experiences with campus and building-scale location systems to identify the technological and social barriers to a truly ubiquitous deployment. With a grasp of these "barriers to adoption," we present a usage scenario, the problems in realizing this scenario, and how these problems will be addressed. We conclude with a sketch of the multi-organization cooperative being formed to move this effort forward.
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paper_title: Reliability considerations of multi-sensor multi-network pedestrian navigation

paper_content:

This study discusses a simple multi-sensor multi-network positioning system that integrates global positioning satellite (GPS) measurements, accelerometers and a digital compass with wireless network localisation utilising a pedestrian motion model and dead reckoning. The feasibility of the multi-technology system for seamless outdoor to indoor pedestrian navigation is discussed with the emphasis on reliability issues and adaptability requirements. The multi-sensor multi-network positioning system is developed for challenging navigation environments such as indoors and deep urban canyons. This study considers how to estimate and improve such a multi-sensor multi-network system's reliability and estimate its accuracy. An outdoor to indoor pedestrian test is conducted. Adaptive filtering performance of the multi-technology solution as well as general measurement quality monitoring and error detection when an over-determined solution is at hand is shown. Reliability estimation utilising adaptation in the form of environment detection to estimate the final positioning accuracy is also presented.
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paper_title: A Hybrid Smartphone Indoor Positioning Solution for Mobile LBS

paper_content:

Smartphone positioning is an enabling technology used to create new business in the navigation and mobile location-based services (LBS) industries. This paper presents a smartphone indoor positioning engine named HIPE that can be easily integrated with mobile LBS. HIPE is a hybrid solution that fuses measurements of smartphone sensors with wireless signals. The smartphone sensors are used to measure the user’s motion dynamics information (MDI), which represent the spatial correlation of various locations. Two algorithms based on hidden Markov model (HMM) problems, the grid-based filter and the Viterbi algorithm, are used in this paper as the central processor for data fusion to resolve the position estimates, and these algorithms are applicable for different applications, e.g., real-time navigation and location tracking, respectively. HIPE is more widely applicable for various motion scenarios than solutions proposed in previous studies because it uses no deterministic motion models, which have been commonly used in previous works. The experimental results showed that HIPE can provide adequate positioning accuracy and robustness for different scenarios of MDI combinations. HIPE is a cost-efficient solution, and it can work flexibly with different smartphone platforms, which may have different types of sensors available for the measurement of MDI data. The reliability of the positioning solution was found to increase with increasing precision of the MDI data.
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paper_title: Growing an organic indoor location system

paper_content:

Most current methods for 802.11-based indoor localization depend on surveys conducted by experts or skilled technicians. Some recent systems have incorporated surveying by users. Structuring localization systems "organically," however, introduces its own set of challenges: conveying uncertainty, determining when user input is actually required, and discounting erroneous and stale data. Through deployment of an organic location system in our nine-story building, which contains nearly 1,400 distinct spaces, we evaluate new algorithms for addressing these challenges. We describe the use of Voronoi regions for conveying uncertainty and reasoning about gaps in coverage, and a clustering method for identifying potentially erroneous user data. Our algorithms facilitate rapid coverage while maintaining positioning accuracy comparable to that achievable with survey-driven indoor deployments.
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paper_title: Redpin - adaptive, zero-configuration indoor localization through user collaboration

paper_content:

Redpin is a fingerprint-based indoor localization system designed and built to run on mobile phones. The basic principles of our system are based on known systems like Place Lab or Radar. However, with Redpin it is possible to consider the signal-strength of GSM, Bluetooth, and WiFi access points on a mobile phone. Moreover, we devised methods to omit the time-consuming training phase and instead incorporate a folksonomy-like approach where the users train the system while using it. Finally, this approach also enables the system to expeditiously adapt to changes in the environment, caused for example by replaced access points.
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paper_title: WiFi-SLAM Using Gaussian Process Latent Variable Models

paper_content:

WiFi localization, the task of determining the physical location of a mobile device from wireless signal strengths, has been shown to be an accurate method of indoor and outdoor localization and a powerful building block for location-aware applications. However, most localization techniques require a training set of signal strength readings labeled against a ground truth location map, which is prohibitive to collect and maintain as maps grow large. In this paper we propose a novel technique for solving the WiFi SLAM problem using the Gaussian Process Latent Variable Model (GPLVM) to determine the latent-space locations of unlabeled signal strength data. We show how GPLVM, in combination with an appropriate motion dynamics model, can be used to reconstruct a topological connectivity graph from a signal strength sequence which, in combination with the learned Gaussian Process signal strength model, can be used to perform efficient localization.
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paper_title: Crowdsourcing with Smartphones

paper_content:

Smartphones can reveal crowdsourcing's full potential and let users transparently contribute to complex and novel problem solving. This emerging area is illustrated through a taxonomy that classifies the mobile crowdsourcing field and through three new applications that optimize location-based search and similarity services based on crowd-generated data. Such applications can be deployed on SmartLab, a cloud of more than 40 Android devices deployed at the University of Cyprus that provides an open testbed to facilitate research and development of smartphone applications on a massive scale.
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paper_title: Zee: zero-effort crowdsourcing for indoor localization

paper_content:

Radio Frequency (RF) fingerprinting, based onWiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee -- a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the user's initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length,(c) back propagation to go back and improve the accuracy of ocalization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building.
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paper_title: Collaborative Pedestrian Mapping of Buildings Using Inertial Sensors and FootSLAM

paper_content:

The FeetSLAM technique builds on iterative processing of multiple sets of pedestrian odometry data, based on FootSLAM. The objective is to obtain maps of large areas based on many data sets. The central idea is that maps originating from other data sets are used as a so-called prior map for a given data set. We show that this follows from the optimal FeetSLAM derivation but is more suited to practical computation limitations such as limited memory. It also yields maps which are not overly dominated by one data set but rather balances the characteristics of each with the effect of averaging out errors. Over iterations, FootSLAM maps are gradually combined to yield a high-accuracy global map the iteration speed is controlled by employing concepts from simulated annealing. We validate our approach using two data sets from two locations, consisting of four and five walks respectively.
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paper_title: Simultaneous Localization and Mapping for pedestrians using distortions of the local magnetic field intensity in large indoor environments

paper_content:

We present a Simultaneous Localization and Mapping (SLAM) algorithm based on measurements of the ambient magnetic field strength (MagSLAM) that allows quasi-real-time mapping and localization in buildings, where pedestrians with foot-mounted sensors are the subjects to be localized. We assume two components to be present: firstly a source of odometry (human step measurements), and secondly a sensor of the local magnetic field intensity. Our implementation follows the FastSLAM factorization using a particle filter. We augment the hexagonal transition map used in the pre-existing FootSLAM algorithm with local maps of the magnetic field strength, binned in a hierarchical hexagonal structure. We performed extensive experiments in a number of different buildings and present the results for five data sets for which we have ground truth location information. We consider the results obtained using MagSLAM to be strong evidence that scalable and accurate localization is possible without an a priori map.
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paper_title: Locating in fingerprint space: wireless indoor localization with little human intervention

paper_content:

Indoor localization is of great importance for a range of pervasive applications, attracting many research efforts in the past decades. Most radio-based solutions require a process of site survey, in which radio signatures of an interested area are annotated with their real recorded locations. Site survey involves intensive costs on manpower and time, limiting the applicable buildings of wireless localization worldwide. In this study, we investigate novel sensors integrated in modern mobile phones and leverage user motions to construct the radio map of a floor plan, which is previously obtained only by site survey. On this basis, we design LiFS, an indoor localization system based on off-the-shelf WiFi infrastructure and mobile phones. LiFS is deployed in an office building covering over 1600m2, and its deployment is easy and rapid since little human intervention is needed. In LiFS, the calibration of fingerprints is crowdsourced and automatic. Experiment results show that LiFS achieves comparable location accuracy to previous approaches even without site survey.
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paper_title: HiMLoc: Indoor smartphone localization via activity aware Pedestrian Dead Reckoning with selective crowdsourced WiFi fingerprinting

paper_content:

The large number of applications that rely on indoor positioning encourages more advancement in this field. Smartphones are becoming a common presence in our daily life, so taking advantage of their sensors can help to provide ubiquitous positioning solution. We propose HiMLoc, a novel solution that synergistically uses Pedestrian Dead Reckoning (PDR) and WiFi fingerprinting to exploit their positive aspects and limit the impact of their negative aspects. Specifically, HiMLoc combines location tracking and activity recognition using inertial sensors on mobile devices with location-specific weighted assistance from a crowd-sourced WiFi fingerprinting system via a particle filter. By using just the most common sensors available on the large majority of smartphones (accelerometer, compass, and WiFi card) and offering an easily deployable method (requiring just the locations of stairs, elevators, corners and entrances), HiMLoc is shown to achieve median accuracies lower than 3 meters in most cases.
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paper_title: No need to war-drive: unsupervised indoor localization

paper_content:

We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that "sense" these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1:69m. War-driving is not necessary, neither are floorplans the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment.
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paper_title: Elekspot: A Platform for Urban Place Recognition via Crowdsourcing

paper_content:

The proliferation of Wi-Fi infrastructures has facilitated numerous indoor localization techniques using Wi-Fi location fingerprints. They make it possible to identify a room or a place in urban environment, which is especially important in enabling many interesting location-based services. As there are too many rooms and places such as cafes and restaurants to be recognized in urban environment, the crowdsourcing approach has been proposed to collect Wi-Fi location fingerprints based on user participation. However, its actual deployment in a large-scale urban environment presents numerous design and implementation challenges due to urban characteristics such as a large crowd, dense region, and device diversity. This paper presents Elekspot system, whose design goal is to support system scalability, device heterogeneity, robustness against lack of contributions, and localization accuracy. Through several experiments and implementation of actual applications targeting urban places we confirmed that the architecture and methods of Elekspot can effectively meet the design goals.
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paper_title: FreeLoc: Calibration-free crowdsourced indoor localization

paper_content:

Many indoor localization techniques that rely on RF signals from wireless Access Points have been proposed in the last decade. In recent years, research on crowdsourced (also known as “Organic”) Wi-Fi fingerprint positioning systems has been attracting much attention. This participatory approach introduces new challenges that no previously proposed techniques have taken into account. This paper proposes “FreeLoc”, an efficient localization method addressing three major technical issues posed in crowdsourcing based systems. Our novel solution facilitates 1) extracting accurate fingerprint values from short RSS measurement times 2) calibration-free positioning across different devices and 3) maintaining a single fingerprint for each location in a radio map, irrespective of any number of uploaded data sets for a given location. Through experiments using four different smartphones, we evaluate our new indoor positioning method. The experimental results confirm that the proposed scheme provides consistent localization accuracy in an environment where the device heterogeneity and the multiple surveyor problems exist.
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paper_title: WicLoc: An indoor localization system based on WiFi fingerprints and crowdsourcing

paper_content:

WiFi fingerprint-based indoor localization techniques have been proposed and widely used in recent years. Most solutions need a site survey to collect fingerprints from interested locations to construct the fingerprint database. However, the site survey is labor-intensive and time-consuming. To overcome this shortcoming, we record user motions as well as WiFi signals without the active participation of the users to construct the fingerprint database, in place of the previous site survey. In this paper, we develop an indoor localization system called WicLoc, which is based on WiFi fingerprinting and crowdsourcing. We design a fingerprint model to form fingerprints of each location of interest after fingerprint collection. We propose a weighted KNN (K-Nearest Neighbor) algorithm to assign different weights to APs and achieve room-level localization. To obtain the absolute coordinate of users, we design a novel MDS (Multi-Dimensional Scaling) algorithm called MDS-C (Multi-Dimensional Scaling with Calibrations) to calculate coordinates of interested locations in the corridor and rooms, where anchor points are used to calibrate absolute coordinates of users. Experimental results show that our system can achieve a competitive localization accuracy compared with state-of-the-art WiFi fingerprint-based methods while avoiding the labor-intensive site survey.
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paper_title: Rank based fingerprinting algorithm for indoor positioning

paper_content:

A novel Received Signal Strength (RSS) rank based fingerprinting algorithm for indoor positioning is presented. Because RSS rank is invariant to bias and scaling, the algorithm provides the same accuracy for any receiver device, without the need for RSS calibration. Similarity measures to compare ranked vectors are introduced and their impact on positioning accuracy is investigated in experiments. Experimental results shown that proposed algorithm can achieve better accuracy than commonly used NN and WKNN fingerprinting algorithms.
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Title: A Survey of Crowd Sensing Opportunistic Signals for Indoor Localization

Section 1: Introduction
Description 1: This section provides background information on mobile indoor localization and crowd sensing, setting the context for the survey.

Section 2: Foundation of Crowd Sensing for Indoor Localization
Description 2: This section describes the basic principles of crowd sensing and its application in fingerprinting-based indoor localization, including the learning and positioning phases.

Section 3: Scheme of a Crowd Sensing Based Mobile Indoor Localization Approach
Description 3: This section outlines the structure of a crowd sensing-based localization system, focusing on the roles of the frontend and backend components.

Section 4: Crowd Sensing versus Expert Survey
Description 4: This section compares crowd sensing with traditional expert surveys, evaluating factors like time consumption, labor cost, data quality, and more.

Section 5: Opportunistic Signals
Description 5: This section examines various types of opportunistic signals used for fingerprinting-based localization, including Wi-Fi, Bluetooth, magnetic fields, image features, and others.

Section 6: Walking Trajectory
Description 6: This section discusses methods for obtaining the walking trajectory of participants, focusing on Pedestrian Dead Reckoning (PDR) and Simultaneous Localization and Mapping (SLAM).

Section 7: Indoor Maps
Description 7: This section explores the use of indoor maps, such as raster and vector maps, in indoor localization.

Section 8: Organic Fingerprint
Description 8: This section defines the concept of organic fingerprints and discusses the challenges and solutions related to data fusion in crowd sensing.

Section 9: Fingerprinting-Based Positioning Algorithms
Description 9: This section reviews various positioning algorithms, including deterministic, probabilistic, and hybrid solutions for indoor localization.

Section 10: The State-of-the-Art Solutions
Description 10: This section presents a comparison of recent state-of-the-art solutions in crowd sensing-based indoor localization.

Section 11: Challenges
Description 11: This section outlines the challenges facing crowd sensing-based indoor localization, such as device diversity, quality control, power consumption, and privacy protection.

Section 12: Conclusion and Future Trends
Description 12: This section concludes the survey by summarizing the findings and discussing future research directions in crowd sensing-based indoor localization.