When Things Matter : A Survey on Data-Centric Internet of Things
---
paper_title: Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things

paper_content:

The traditional view of Internet of Things (IoT) attempts to connect all the physical objects to build a global, infrastructure-based IoT. In this paper, however, we will present opportunistic IoT, which is formed based on the ad hoc, opportunistic networking of devices (e.g., mobile phones and smart vehicles) using short-range radio techniques (e.g., Bluetooth and Wi-Fi). The opportunistic IoT demonstrates inherently the close relationship between human and opportunistic connection of smart things. It enables information forwarding and dissemination within and among the opportunistic communities formed based on the movement and opportunistic contact nature of human. We characterize the bi-directional effects between human and opportunistic IoT, discuss the technical challenges faced by this new research field, and propose a reference architecture for developing opportunistic IoT systems. Some of our ongoing practices, including opportunistic mobile social networking, opportunistic marketing, and community service provision are further presented to demonstrate the potential application areas and technical solutions of opportunistic IoT.
---
paper_title: Context Aware Computing for The Internet of Things: A Survey

paper_content:

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.
---
paper_title: The internet of things: a survey

paper_content:

In recent year, the Internet of Things (IoT) has drawn significant research attention. IoT is considered as a part of the Internet of the future and will comprise billions of intelligent communicating `things'. The future of the Internet will consist of heterogeneously connected devices that will further extend the borders of the world with physical entities and virtual components. The Internet of Things (IoT) will empower the connected things with new capabilities. In this survey, the definitions, architecture, fundamental technologies, and applications of IoT are systematically reviewed. Firstly, various definitions of IoT are introduced; secondly, emerging techniques for the implementation of IoT are discussed; thirdly, some open issues related to the IoT applications are explored; finally, the major challenges which need addressing by the research community and corresponding potential solutions are investigated.
---
paper_title: Research on social relations cognitive model of mobile nodes in Internet of Things

paper_content:

Interaction and communication between humans with smart mobile devices are a new trend of development in Internet of Things (IoT). With the powerful sensing capability of smart device and human mobility, various services could be provided by building a trusted chain between service requesters and suppliers. The cognition of social relations between mobile nodes is the basis of final mobile-aware services. It involves many decision factors, such as time, space and activity patterns. Using social network theory, a new cognitive model for social relations of mobile nodes in IoT is proposed. Firstly, nodes' social relations are reasoned and quantified from multiple perspectives based on the summary of social characteristics of mobile nodes and the definition of different decision factors. Then the location factor, interconnection factor, service evaluation factor and feedback aggregation factor are defined to solve the shortcomings in existing quantitative models. Finally, the weight distribution is set up by information entropy and rough set theory for these decision factors; it can overcome the shortage of traditional methods, in which the weight is set up by subjective ways and hence their dynamic adaptability is poor. We compare our cognitive model to existing models using MIT dataset by defining a variety of test indicators, such as network overall density (NOD), the degree center potential (DCP), the network distribution index (EI), etc. Simulation results show that, the cognitive model has better internal structure and significant validity in network analysis, and thus can provide mobile-aware service effectively in dynamic environment.
---
paper_title: The Internet of Things: A survey

paper_content:

This paper addresses the Internet of Things. Main enabling factor of this promising paradigm is the integration of several technologies and communications solutions. Identification and tracking technologies, wired and wireless sensor and actuator networks, enhanced communication protocols (shared with the Next Generation Internet), and distributed intelligence for smart objects are just the most relevant. As one can easily imagine, any serious contribution to the advance of the Internet of Things must necessarily be the result of synergetic activities conducted in different fields of knowledge, such as telecommunications, informatics, electronics and social science. In such a complex scenario, this survey is directed to those who want to approach this complex discipline and contribute to its development. Different visions of this Internet of Things paradigm are reported and enabling technologies reviewed. What emerges is that still major issues shall be faced by the research community. The most relevant among them are addressed in details.
---
paper_title: Research Directions in Database Architectures for the Internet of Things: A Communication of the First International Workshop on Database Architectures for the Internet of Things (DAIT 2009)

paper_content:

This paper is a record of the First International Workshop on Database Architectures for the Internet of Things. The Internet of Things refers to the future internet which will contain trillions of nodes representing various objects from small ubiquitous sensor devices and handhelds to large web servers and supercomputer clusters. The workshop investigated a number of areas appertaining to data management in the Internet of Things from storage structures, through database management methods, to service-oriented architectures and new approaches to information search. Running orthogonal to these layers the matter of security was also considered. Taking a philosophical viewpoint our whole current framework for understanding data management may be ill-equipped to meet the challenges of the Internet of Things. The workshop gave participants the chance to discuss these matters, exchange ideas and explore future collaborations.
---
paper_title: Data stream clustering: A survey

paper_content:

Data stream mining is an active research area that has recently emerged to discover knowledge from large amounts of continuously generated data. In this context, several data stream clustering algorithms have been proposed to perform unsupervised learning. Nevertheless, data stream clustering imposes several challenges to be addressed, such as dealing with nonstationary, unbounded data that arrive in an online fashion. The intrinsic nature of stream data requires the development of algorithms capable of performing fast and incremental processing of data objects, suitably addressing time and memory limitations. In this article, we present a survey of data stream clustering algorithms, providing a thorough discussion of the main design components of state-of-the-art algorithms. In addition, this work addresses the temporal aspects involved in data stream clustering, and presents an overview of the usually employed experimental methodologies. A number of references are provided that describe applications of data stream clustering in different domains, such as network intrusion detection, sensor networks, and stock market analysis. Information regarding software packages and data repositories are also available for helping researchers and practitioners. Finally, some important issues and open questions that can be subject of future research are discussed.
---
paper_title: A SURVEY OF STREAM PROCESSING PROBLEMS AND TECHNIQUES IN SENSOR NETWORKS

paper_content:

Sensor networks comprise small, low-powered and low-cost sensing devices that are distributed over a field to monitor a phenomenon of interest. The sensor nodes are capable of communicating their readings, typically through wireless radio. Sensor nodes produce streams of data, that have to be processed in-situ, by the node itself, or to be transmitted through the network, and analyzed offline. In this chapter we describe recently proposed, efficient distributed techniques for processing streams of data collected with a network of sensors.
---
paper_title: A SURVEY OF STREAM PROCESSING PROBLEMS AND TECHNIQUES IN SENSOR NETWORKS

paper_content:

Sensor networks comprise small, low-powered and low-cost sensing devices that are distributed over a field to monitor a phenomenon of interest. The sensor nodes are capable of communicating their readings, typically through wireless radio. Sensor nodes produce streams of data, that have to be processed in-situ, by the node itself, or to be transmitted through the network, and analyzed offline. In this chapter we describe recently proposed, efficient distributed techniques for processing streams of data collected with a network of sensors.
---
paper_title: Distributed top-k monitoring

paper_content:

The querying and analysis of data streams has been a topic of much recent interest, motivated by applications from the fields of networking, web usage analysis, sensor instrumentation, telecommunications, and others. Many of these applications involve monitoring answers to continuous queries over data streams produced at physically distributed locations, and most previous approaches require streams to be transmitted to a single location for centralized processing. Unfortunately, the continual transmission of a large number of rapid data streams to a central location can be impractical or expensive. We study a useful class of queries that continuously report the k largest values obtained from distributed data streams ("top-k monitoring queries"), which are of particular interest because they can be used to reduce the overhead incurred while running other types of monitoring queries. We show that transmitting entire data streams is unnecessary to support these queries and present an alternative approach that reduces communication significantly. In our approach, arithmetic constraints are maintained at remote stream sources to ensure that the most recently provided top-k answer remains valid to within a user-specified error tolerance. Distributed communication is only necessary on occasion, when constraints are violated, and we show empirically through extensive simulation on real-world data that our approach reduces overall communication cost by an order of magnitude compared with alternatives that o er the same error guarantees.
---
paper_title: Algorithms for Mining Distance-Based Outliers in Large Datasets

paper_content:

This paper deals with finding outliers (exceptions) in large, multidimensional datasets. The identification of outliers can lead to the discovery of truly unexpected knowledge in areas such as electronic commerce, credit card fraud, and even the analysis of performance statistics of professional athletes. Existing methods that we have seen for finding outliers in large datasets can only deal efficiently with two dimensions/attributes of a dataset. Here, we study the notion of DB- (DistanceBased) outliers. While we provide formal and empirical evidence showing the usefulness of DB-outliers, we focus on the development of algorithms for computing such outliers. First, we present two simple algorithms, both having a complexity of O(k N’), k being the dimensionality and N being the number of objects in the dataset. These algorithms readily support datasets with many more than two attributes. Second, we present an optimized cell-based algorithm that has a complexity that is linear wrt N, but exponential wrt k. Third, for datasets that are mainly disk-resident, we present another version of the cell-based algorithm that guarantees at most 3 passes over a dataset. We provide
---
paper_title: Enabling Next-Generation RFID Applications: Solutions and Challenges

paper_content:

Radio-frequency identification technology provides promising benefits such as inventory visibility and business process automation. However, if companies are to realize these benefits, researchers must address major challenges such as data processing, integration architecture design, security, and privacy.
---
paper_title: Networked RFID: Systems, Software and Services

paper_content:

RFID technology has been available for decades, but it is only recently that its application has become a core topic for computing. This book introduces the technologies and techniques of large-scale RFID-enabled mobile computing systems, set in the context of specific case studies. The book begins with a quick introduction to RFID basics and then discusses the various elements of the topic, using applications such as e-passports, ticketing and supply-chain management throughout. The text explores RFID technology fundamentals, including operating principles, core system components and performance trade-offs involved in the selection of specific RFID platforms. The emphasis is on a practical approach, developing an arsenal of techniques and designs that can be mixed and matched to fit the needs of new systems and applications. Features Offers a practical approach, with specific RFID applications providing context for discussion Highlights the basics of RFID, including wireless energy transfer and communication by reflection. Presents the main ingredients for building complete network RFID systems Discusses the different types of readers and tags in detail making particular reference to common technologies used in real systems Explores how the different identifier systems encode unique serial codes for tagged entities, and how codes are assigned to particular organizations Describes the structure and functionality of modern RFID systems and middleware Considers different types of network services required for RFID support Debates the hot topics of privacy and security Looks at the future of RFID technology Allows readers to experiment via a supplementary website with operational instances of RFID network services: http://www.roussos.eu/rfid Written for IT professionals as well as students, this comprehensive and reader-friendly text discusses the current state-of-the-art in RFID platforms and architecture. It will be invaluable for anyone experienced in software development and involved with RFID technology, and will facilitate informed decision-making.
---
paper_title: Ubiquitous ID: Standards for Ubiquitous Computing and the Internet of Things

paper_content:

In ubiquitous computing environments, many tiny computers cooperate, adapting their behaviors according to real-world contexts to provide flexible information services. These information services can't be realized without technology standardization in a broad area. In this article, we discuss the principle of ubiquitous computing technology standardization and introduce uID standards currently in development.
---
paper_title: KLEAP: an efficient cleaning method to remove cross-reads in RFID streams

paper_content:

Recently, the RFID technology has been widely used in many kinds of applications. However, because of the interference from environmental factors and limitations of the radio frequency technology, the data streams collected by the RFID readers are usually contain a lot of cross-reads. To address this issue, we propose a KerneL dEnsity-bAsed Probability cleaning method (KLEAP) to remove cross-reads within a sliding window. The method estimates the density of each tag using a kernel-based function. The reader corresponding to the micro-cluster with the largest density will be regarded as the position that the tagged object should locate in current window, and the readings derived from other readers will be treated as the cross-reads. Experiments verify the effectiveness and efficiency of the proposed method.
---
paper_title: Cleaning trajectory data of RFID-monitored objects through conditioning under integrity constraints

paper_content:

A probabilistic framework is introduced for reducing the inherent uncertainty of trajectory data collected for RFID-monitored objects. The framework represents the position of an object at each instant as a random variable over the set of possible locatio ns. The probability density function of this random variable is ini tialized according to an a-priori probability distribution, and the n revised by conditioning it w.r.t. the event that integrity constrai nts are satisfied. In particular, integrity constraints implied by the structure of the map of locations and the motility characteristics (su ch as the maximum speed) of the monitored objects are exploited (namely, direct unreachability, latency and minimum traveling timeconstraints). The efficiency and effectiveness of the proposed approach ar e assessed experimentally on synthetic data.
---
paper_title: Adaptive Cleaning for RFID Data Streams

paper_content:

To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a "smoothing filter", a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications.
---
paper_title: SPIRE: Efficient Data Inference and Compression over RFID Streams

paper_content:

Despite its promise, RFID technology presents numerous challenges, including incomplete data, lack of location and containment information, and very high volumes. In this work, we present a novel data inference and compression substrate over RFID streams to address these challenges. Our substrate employs a time-varying graph model to efficiently capture possible object locations and interobject relationships such as containment from raw RFID streams. It then employs a probabilistic algorithm to estimate the most likely location and containment for each object. By performing such online inference, it enables online compression that recognizes and removes redundant information from the output stream of this substrate. We have implemented a prototype of our inference and compression substrate and evaluated it using both real traces from a laboratory warehouse setup and synthetic traces emulating enterprise supply chains. Results of a detailed performance study show that our data inference techniques provide high accuracy while retaining efficiency over RFID data streams, and our compression algorithm yields significant reduction in output data volume.
---
paper_title: Distributed Inference and Query Processing for RFID Tracking and Monitoring

paper_content:

In this paper, we present the design of a scalable, distributed stream processing system for RFID tracking and monitoring. Since RFID data lacks containment and location information that is key to query processing, we propose to combine location and containment inference with stream query processing in a single architecture, with inference as an enabling mechanism for high-level query processing. We further consider challenges in instantiating such a system in large distributed settings and design techniques for distributed inference and query processing. Our experimental results, using both real-world data and large synthetic traces, demonstrate the accuracy, efficiency, and scalability of our proposed techniques.
---
paper_title: Probabilistic Inference over RFID Streams in Mobile Environments

paper_content:

Recent innovations in RFID technology are enabling large-scale cost-effective deployments in retail, healthcare, pharmaceuticals and supply chain management. The advent of mobile or handheld readers adds significant new challenges to RFID stream processing due to the inherent reader mobility, increased noise, and incomplete data. In this paper, we address the problem of translating noisy, incomplete raw streams from mobile RFID readers into clean, precise event streams with location information. Specifically we propose a probabilistic model to capture the mobility of the reader, object dynamics, and noisy readings. Our model can self-calibrate by automatically estimating key parameters from observed data. Based on this model, we employ a sampling-based technique called particle filtering to infer clean, precise information about object locations from raw streams from mobile RFID readers. Since inference based on standard particle filtering is neither scalable nor efficient in our settings, we propose three enhancements---particle factorization, spatial indexing, and belief compression---for scalable inference over large numbers of objects and high-volume streams. Our experiments show that our approach can offer 49\% error reduction over a state-of-the-art data cleaning approach such as SMURF while also being scalable and efficient.
---
paper_title: SRBench: a streaming RDF/SPARQL benchmark

paper_content:

We introduce SRBench, a general-purpose benchmark primarily designed for streaming RDF/SPARQL engines, completely based on real-world data sets from the Linked Open Data cloud. With the increasing problem of too much streaming data but not enough tools to gain knowledge from them, researchers have set out for solutions in which Semantic Web technologies are adapted and extended for publishing, sharing, analysing and understanding streaming data. To help researchers and users comparing streaming RDF/SPARQL (strRS) engines in a standardised application scenario, we have designed SRBench, with which one can assess the abilities of a strRS engine to cope with a broad range of use cases typically encountered in real-world scenarios. The data sets used in the benchmark have been carefully chosen, such that they represent a realistic and relevant usage of streaming data. The benchmark defines a concise, yet comprehensive set of queries that cover the major aspects of strRS processing. Finally, our work is complemented with a functional evaluation on three representative strRS engines: SPARQLStream, C-SPARQL and CQELS. The presented results are meant to give a first baseline and illustrate the state-of-the-art.
---
paper_title: An execution environment for C-SPARQL queries

paper_content:

Continuous SPARQL (C-SPARQL) is proposed as new language for continuous queries over streams of RDF data. It covers a gap in the Semantic Web abstractions which is needed for many emerging applications, including our focus on Urban Computing. In this domain, sensor-based information on roads must be processed to deduce localized traffic conditions and then produce traffic management strategies. Executing C-SPARQL queries requires the effective integration of SPARQL and streaming technologies, which capitalize over a decade of research and development; such integration poses several nontrivial challenges. In this paper we (a) show the syntax and semantics of the C-SPARQL language together with some examples; (b) introduce a query graph model which is an intermediate representation of queries devoted to optimization; (c) discuss the features of an execution environment that leverages existing technologies; (d) introduce optimizations in terms of rewriting rules applied to the query graph model, so as to efficiently exploit the execution environment; and (e) show evidence of the effectiveness of our optimizations on a prototype of execution environment.
---
paper_title: Streaming SPARQL extending SPARQL to process data streams

paper_content:

A lot of work has been done in the area of data stream processing. Most of the previous approaches regard only relational or XML based streams but do not cover semantically richer RDF based stream elements. In our work, we extend SPARQL, the W3C recommendation for an RDF query language, to process RDF data streams. To describe the semantics of our enhancement, we extended the logical SPARQL algebra for stream processing on the foundation of a temporal relational algebra based on multi-sets and provide an algorithm to transform SPARQL queries to the new extended algebra. For each logical algebra operator, we define executable physical counterparts. To show the feasibility of our approach, we implemented it within our ODYSSEUS framework in the context of wind power plant monitoring.
---
paper_title: EP-SPARQL: A Unified Language for Event Processing and Stream Reasoning

paper_content:

Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams. To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL) as a new language for complex events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic programming, and features effective event processing and inferencing capabilities over temporal and static knowledge. We provide an open-source prototype implementation and present a set of tests to show the usefulness and effectiveness of our approach.
---
paper_title: Enabling ontology-based access to streaming data sources

paper_content:

The availability of streaming data sources is progressively increasing thanks to the development of ubiquitous data capturing technologies such as sensor networks. The heterogeneity of these sources introduces the requirement of providing data access in a unified and coherent manner, whilst allowing the user to express their needs at an ontological level. In this paper we describe an ontology-based streaming data access service. Sources link their data content to ontologies through S2O mappings. Users can query the ontology using SPARQLStream, an extension of SPARQL for streaming data. A preliminary implementation of the approach is also presented. With this proposal we expect to set the basis for future efforts in ontology-based streaming data integration.
---
paper_title: Real-Time RDF Extraction from Unstructured Data Streams

paper_content:

The vision behind the Web of Data is to extend the current document-oriented Web with machine-readable facts and structured data, thus creating a representation of general knowledge. However, most of the Web of Data is limited to being a large compendium of encyclopedic knowledge describing entities. A huge challenge, the timely and massive extraction of RDF facts from unstructured data, has remained open so far. The availability of such knowledge on the Web of Data would provide significant benefits to manifold applications including news retrieval, sentiment analysis and business intelligence. In this paper, we address the problem of the actuality of the Web of Data by presenting an approach that allows extracting RDF triples from unstructured data streams. We employ statistical methods in combination with deduplication, disambiguation and unsupervised as well as supervised machine learning techniques to create a knowledge base that reflects the content of the input streams. We evaluate a sample of the RDF we generate against a large corpus of news streams and show that we achieve a precision of more than 85%.
---
paper_title: C-Store: A Column-oriented DBMS

paper_content:

This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of column-oriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures.We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging.
---
paper_title: The End of an Architectural Era (It's Time for a Complete Rewrite)

paper_content:

In previous papers [SC05, SBC+07], some of us predicted the end of "one size fits all" as a commercial relational DBMS paradigm. These papers presented reasons and experimental evidence that showed that the major RDBMS vendors can be outperformed by 1--2 orders of magnitude by specialized engines in the data warehouse, stream processing, text, and scientific database markets. ::: ::: Assuming that specialized engines dominate these markets over time, the current relational DBMS code lines will be left with the business data processing (OLTP) market and hybrid markets where more than one kind of capability is required. In this paper we show that current RDBMSs can be beaten by nearly two orders of magnitude in the OLTP market as well. The experimental evidence comes from comparing a new OLTP prototype, H-Store, which we have built at M.I.T. to a popular RDBMS on the standard transactional benchmark, TPC-C. ::: ::: We conclude that the current RDBMS code lines, while attempting to be a "one size fits all" solution, in fact, excel at nothing. Hence, they are 25 year old legacy code lines that should be retired in favor of a collection of "from scratch" specialized engines. The DBMS vendors (and the research community) should start with a clean sheet of paper and design systems for tomorrow's requirements, not continue to push code lines and architectures designed for yesterday's needs.
---
paper_title: Brewer's conjecture and the feasibility of consistent, available, partition-tolerant web services

paper_content:

When designing distributed web services, there are three properties that are commonly desired: consistency, availability, and partition tolerance. It is impossible to achieve all three. In this note, we prove this conjecture in the asynchronous network model, and then discuss solutions to this dilemma in the partially synchronous model.
---
paper_title: Big Data: A Survey

paper_content:

In this paper, we review the background and state-of-the-art of big data. We first introduce the general background of big data and review related technologies, such as could computing, Internet of Things, data centers, and Hadoop. We then focus on the four phases of the value chain of big data, i.e., data generation, data acquisition, data storage, and data analysis. For each phase, we introduce the general background, discuss the technical challenges, and review the latest advances. We finally examine the several representative applications of big data, including enterprise management, Internet of Things, online social networks, medial applications, collective intelligence, and smart grid. These discussions aim to provide a comprehensive overview and big-picture to readers of this exciting area. This survey is concluded with a discussion of open problems and future directions.
---
paper_title: Programming storage-centric sensor networks with Squirrel

paper_content:

We present Squirrel, a stream-oriented programming framework for storage-centric sensor networks. The storage-centric paradigm---where storage operations prevail over communication activity---applies to scenarios such as batch data collection, delay-tolerant mobile applications, and disconnected operations in static networks. Squirrel simplifies developing such applications by decoupling data processing from storage, and by transparently handling the latter. We achieve this through: i) a modular programming abstraction, and ii) a lightweight run-time layer that efficiently allocates data to different storage areas, based on size vs. energy tradeoffs. We demonstrate Squirrel's effectiveness based on three real-world applications, each representing a different storage-centric scenario. The results show that---while relieving programmers from a significant burden---Squirrel achieves efficient utilization of storage areas, enabling energy savings independently of the storage technology.
---
paper_title: Energy efficient sensor data logging with amnesic flash storage

paper_content:

We present FlashLogger, an energy-efficient sensor data logging system that uses lazy amnesic compression in a flash-efficient manner. FlashLogger incorporates a suite of compression algorithms suitable for progressively compressing time series scalar, audio, and image data. It uses a novel data structure for efficiently organizing and querying compressed data on flash memory. All our methods are designed for the limited memory and processing capabilities typical of low power sensor nodes, and are prototyped on Tmote Sky platform running TinyOS. Evaluation of FlashLogger with several real world data sets shows orders of magnitude energy savings for both logging data and retrieving data within a time range.
---
paper_title: A database in every sensor

paper_content:

We make the case for a sensor network model in which each mote stores sensor data locally, and provides a database query interface to the data. Unlike TinyDB and Cougar, in which a sink node provides a database-like front end for filtering the current sensor values from a data collection network, we propose that each sensor device should run its own database system. We present Antelope, a database management system for resource-constrained sensors. Antelope provides a dynamic database system that enables run-time creation and deletion of databases and indexes. Antelope uses energy-efficient indexing techniques that significantly improve the performance of queries. The energy cost of a query that selects 100 tuples is less than the cost of a single packet transmission. Moving forward, we believe that database techniques will be increasingly important in many emerging applications.
---
paper_title: SolarStore: enhancing data reliability in solar-powered storage-centric sensor networks

paper_content:

In this paper, we present a reliable storage service, called SolarStore, that adaptively trades-off storage reliability versus energy consumption in solar-powered sensor networks. SolarStore adopts a predominantly disconnected network model, where long-running data-collection experiments are conducted in the absence of a continuous connection to the outside world. SolarStore (i) replicates data in the network until the next upload opportunity, and (ii) adapts the degree of data replication dynamically depending on solar energy and storage availability. The goal is to maximize the amount of data that can eventually be retrieved from the network subject to energy and storage constraints. Maximization of retrievable data implies minimizing sensing blackouts due to energy depletion as well as minimizing loss due to node damage in harsh environmental conditions. We have deployed an outdoor solar-powered sensor network, on which SolarStore is implemented and tested. An indoor testbed is also set up for performance evaluation under environmental conditions not attained locally. Experiments show that SolarStore is successful in dynamically responding to variations in the environment in a manner that increases retrievable data under different node failure scenarios.
---
paper_title: Time is of the essence: improving recency ranking using Twitter data

paper_content:

Realtime web search refers to the retrieval of very fresh content which is in high demand. An effective portal web search engine must support a variety of search needs, including realtime web search. However, supporting realtime web search introduces two challenges not encountered in non-realtime web search: quickly crawling relevant content and ranking documents with impoverished link and click information. In this paper, we advocate the use of realtime micro-blogging data for addressing both of these problems. We propose a method to use the micro-blogging data stream to detect fresh URLs. We also use micro-blogging data to compute novel and effective features for ranking fresh URLs. We demonstrate these methods improve effective of the portal web search engine for realtime web search.
---
paper_title: Earthquake shakes Twitter users: real-time event detection by social sensors

paper_content:

Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes in Twitter and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other comparable methods for estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake with high probability (96% of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected) merely by monitoring tweets. Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA.
---
paper_title: On enhancing scalability for distributed RDF/S stores

paper_content:

This work presents MIDAS-RDF, a distributed P2P RDF/S repository that is built on top of a distributed multi-dimensional index structure. MIDAS-RDF features fast retrieval of RDF triples satisfying various pattern queries by translating them into multi-dimensional range queries, which can be processed by the underlying index in hops logarithmic to the number of peers. More importantly, MIDAS-RDF utilizes a labeling scheme to handle expensive transitive closure computations efficiently. This allows for distributed RDFS reasoning in a more scalable way compared to existing methods, as also demonstrated by our extensive experimental study. Furthermore, MIDAS-RDF supports a publish-subscribe model that enables remote peers to selectively subscribe to RDF content.
---
paper_title: Exploiting web search engines to search structured databases

paper_content:

Web search engines often federate many user queries to relevant structured databases. For example, a product related query might be federated to a product database containing their descriptions and specifications. The relevant structured data items are then returned to the user along with web search results. However, each structured database is searched in isolation. Hence, the search often produces empty or incomplete results as the database may not contain the required information to answer the query. In this paper, we propose a novel integrated search architecture. We establish and exploit the relationships between web search results and the items in structured databases to identify the relevant structured data items for a much wider range of queries.Our architecture leverages existing search engine components to implement this functionality at very low overhead. We demonstrate the quality and efficiency of our techniques through an extensive experimental study.
---
paper_title: Sensor ranking: A primitive for efficient content-based sensor search

paper_content:

The increasing penetration of the real world with embedded and globally networked sensors enables the formation of a Web of Things (WoT), where high-level state information derived from sensors is embedded into Web representations of real-world entities (e.g. places, objects). A key service for the WoT is searching for entities which exhibit a certain dynamic state at the time of the query, which is a challenging problem due to the dynamic nature of the sought state information and due to the potentially huge scale of the WoT. In this paper we introduce a primitive called sensor ranking to enable efficient search for sensors that have a certain output state at the time of the query. The key idea is to efficiently compute for each sensor an estimate of the probability that it matches the query and process sensors in the order of decreasing probability, such that effort is first spent on sensors that are very likely to actually match the query. Using real data sets, we show that sensor ranking can significantly improve the efficiency of content-based sensor search.
---
paper_title: The sensor internet at work: Locating everyday items using mobile phones

paper_content:

We present a system for monitoring and locating everyday items using mobile phones. The system is based on phones which are enhanced with the capability to detect electronically tagged objects in their vicinity. It supports various functionalities: On the one hand, phones can store the context in which users leave registered items and thus help to locate them later on. On the other hand, object owners can search for their objects using the infrastructure of mobile phones carried by other users. We describe the design of our object location system and provide an algorithm which can be used to search for lost or misplaced items efficiently by selecting the most suitable sensors based on arbitrary domain knowledge. Furthermore, we demonstrate the practicability of such wide-area searching by means of user-held sensors in a series of simulations complemented by a real-world experiment.
---
paper_title: Efficient pattern matching over event streams

paper_content:

Pattern matching over event streams is increasingly being employed in many areas including financial services, RFIDbased inventory management, click stream analysis, and electronic health systems. While regular expression matching is well studied, pattern matching over streams presents two new challenges: Languages for pattern matching over streams are significantly richer than languages for regular expression matching. Furthermore, efficient evaluation of these pattern queries over streams requires new algorithms and optimizations: the conventional wisdom for stream query processing (i.e., using selection-join-aggregation) is inadequate. In this paper, we present a formal evaluation model that offers precise semantics for this new class of queries and a query evaluation framework permitting optimizations in a principled way. We further analyze the runtime complexity of query evaluation using this model and develop a suite of techniques that improve runtime efficiency by exploiting sharing in storage and processing. Our experimental results provide insights into the various factors on runtime performance and demonstrate the significant performance gains of our sharing techniques.
---
paper_title: Elastic Complex Event Processing under Varying Query Load

paper_content:

Distributed data stream processing systems, like Twitter Storm or Yahoo! S4, have been primarily focusing on adapting to varying event rates. However, as these systems are becoming increasingly multi-tenant, adaptation to the varying query load is becoming an equally important problem. In this paper we present FUGU – an elastic allocator for Complex Event Processing systems. FUGU uses bin packing to allocate continuous queries to a varying set of nodes. Driven by elasticity requirements FUGU maximizes the overall system utilization while trying to maintain stable processing latencies. The specific contributions of this paper are: (1) introduction of a re-balancing scheme for bin packing allowing FUGU to increase overall system utilization by six percent and (2) a detailed study of achievable system utilization and latency under real-life workload from Frankfurt Stock Exchange.
---
paper_title: High-performance complex event processing over streams

paper_content:

In this paper, we present the design, implementation, and evaluation of a system that executes complex event queries over real-time streams of RFID readings encoded as events. These complex event queries filter and correlate events to match specific patterns, and transform the relevant events into new composite events for the use of external monitoring applications. Stream-based execution of these queries enables time-critical actions to be taken in environments such as supply chain management, surveillance and facility management, healthcare, etc. We first propose a complex event language that significantly extends existing event languages to meet the needs of a range of RFID-enabled monitoring applications. We then describe a query plan-based approach to efficiently implementing this language. Our approach uses native operators to efficiently handle query-defined sequences, which are a key component of complex event processing, and pipeline such sequences to subsequent operators that are built by leveraging relational techniques. We also develop a large suite of optimization techniques to address challenges such as large sliding windows and intermediate result sizes. We demonstrate the effectiveness of our approach through a detailed performance analysis of our prototype implementation under a range of data and query workloads as well as through a comparison to a state-of-the-art stream processor.
---
paper_title: Tutorial: Formal Methods for Event Processing.

paper_content:

Organisations require techniques for automated transformation of the Big Data they collect into operational knowledge. This requirement may be addressed by employing event processing systems that detect activities/events of special significance within an organisation, given streams of low-level information that are difficult to be utilised by humans [4]. Systems for event processing and in particular event recognition (‘event pattern matching’) accept as input a stream of time-stamped, simple or low-level events. A low-level event is the result of applying a computational derivation process to some other event, such as an event coming from a sensor. Using low-level events as input, event processing systems identify composite or high-level events of interest — collections of events that satisfy some pattern. Consider, for example, the recognition of attacks on nodes of a computer network given the TCP/IP messages, the recognition of suspicious trader behaviour given the transactions in a financial market, and the recognition of whale songs given a symbolic representation of whale sounds. Numerous event processing systems have been proposed in the literature [3]. Systems with a logic-based representation of event structures, for example, have been attracting considerable attention. They exhibit a formal, declarative semantics, allowing for verification and a code maintenance, they have proven to be efficient and scalable, and they are supported by machine learning tools, minimising human effort in the system development. In this tutorial, we review formal event processing systems. High-level event ‘definitions’ impose temporal and, possibly, atemporal constraints on subevents, that is, low-level events or other high-level events. We will review a Chronicle Recognition System, the Event Calculus, ProbLog and Markov Logic Networks. The Chronicle Recognition System is a purely temporal reasoning system that allows for efficient event processing. It has been used in various domains, ranging from medical applications to computer network management. The Event Calculus allows for the representation of temporal, as well as atemporal constraints. Consequently, the Event Calculus may be used in applications requiring
---
paper_title: On Load Shedding in Complex Event Processing

paper_content:

Complex Event Processing (CEP) is a stream processing model that focuses on detecting event patterns in continuous event streams. While the CEP model has gained popularity in the research communities and commercial technologies, the problem of gracefully degrading performance under heavy load in the presence of resource constraints, or load shedding, has been largely overlooked. CEP is similar to “classical” stream data management, but addresses a substantially different class of queries. This unfortunately renders the load shedding algorithms developed for stream data processing inapplicable. In this paper we study CEP load shedding under various resource constraints. We formalize broad classes of CEP load-shedding scenarios as different optimization problems. We demonstrate an array of complexity results that reveal the hardness of these problems and construct shedding algorithms with performance guarantees. Our results shed some light on the difficulty of developing load-shedding algorithms that maximize utility.
---
paper_title: Fusion of background knowledge and streams of events

paper_content:

Usage of background knowledge about events and their relations to other concepts in the application domain can improve the expressiveness and flexibility of complex event processing systems. Huge amounts of domain background knowledge stored in external knowledge bases can be used in combination with event processing in order to achieve more knowledgeable complex event processing. In this paper, we discuss the benefits of background knowledge for event processing and describe different categories of event query rules. We propose different event processing approaches used for the fusion of background knowledge with real-time event streams, like plan-based event processing or event query preprocessing. We have implemented some of the proposed event processing methods for the stock market monitoring domain by using available real-time stock events and background knowledge about joint-stock companies from the linked open data. Our experiments show that some of the approaches can improve the performance of event processing compared to more basic approaches.
---
paper_title: Towards an inexact semantic complex event processing framework

paper_content:

Complex event processing (CEP) deals with detecting real-time situations, represented as event patterns, from among an event cloud. The state-of-the-art CEP systems process events as plain data tuples and are limited to detect precisely defined patterns. Emerging application areas like optimization in smart power grids require CEP to incorporate semantic knowledge of the domain for easier pattern specification, and detect inexact patterns in the presence of uncertainties. In this paper, we present motivating use cases, discuss limitations of existing CEP systems and describe our work towards an Inexact Semantic Complex Event Processing (InSCEP) framework.
---
paper_title: Towards unified and native enrichment in event processing systems

paper_content:

Events are encapsulated pieces of information that flow from one event agent to another. In order to process an event, additional information that is external to the event is often needed. This is achieved using a process called event enrichment. Current approaches to event enrichment are external to event processing engines and are handled by specialized agents. Within large-scale environments with high heterogeneity among events, the enrichment process may become difficult to maintain. This paper examines event enrichment in terms of information completeness and presents a unified model for event enrichment that takes place natively within the event processing engine. The paper describes the requirements of event enrichment and highlights its challenges such as finding enrichment sources, retrieval of information items, finding complementary information and its fusion with events. It then details an instantiation of the model using Semantic Web and Linked Data technologies. Enrichment is realised by dynamically guiding a spreading activation algorithm in a Linked Data graph. Multiple spreading activation strategies have been evaluated on a set of Wikipedia events and experimentation shows the viability of the approach.
---
paper_title: Adaptive Cleaning for RFID Data Streams

paper_content:

To compensate for the inherent unreliability of RFID data streams, most RFID middleware systems employ a "smoothing filter", a sliding-window aggregate that interpolates for lost readings. In this paper, we propose SMURF, the first declarative, adaptive smoothing filter for RFID data cleaning. SMURF models the unreliability of RFID readings by viewing RFID streams as a statistical sample of tags in the physical world, and exploits techniques grounded in sampling theory to drive its cleaning processes. Through the use of tools such as binomial sampling and π-estimators, SMURF continuously adapts the smoothing window size in a principled manner to provide accurate RFID data to applications.
---
paper_title: Approximate semantic matching of heterogeneous events

paper_content:

Event-based systems have loose coupling within space, time and synchronization, providing a scalable infrastructure for information exchange and distributed workflows. However, event-based systems are tightly coupled, via event subscriptions and patterns, to the semantics of the underlying event schema and values. The high degree of semantic heterogeneity of events in large and open deployments such as smart cities and the sensor web makes it difficult to develop and maintain event-based systems. In order to address semantic coupling within event-based systems, we propose vocabulary free subscriptions together with the use of approximate semantic matching of events. This paper examines the requirement of event semantic decoupling and discusses approximate semantic event matching and the consequences it implies for event processing systems. We introduce a semantic event matcher and evaluate the suitability of an approximate hybrid matcher based on both thesauri-based and distributional semantics-based similarity and relatedness measures. The matcher is evaluated over a structured representation of Wikipedia and Freebase events. Initial evaluations show that the approach matches events with a maximal combined precision-recall F1 score of 75.89% on average in all experiments with a subscription set of 7 subscriptions. The evaluation shows how a hybrid approach to semantic event matching outperforms a single similarity measure approach.
---
paper_title: Towards an inexact semantic complex event processing framework

paper_content:

Complex event processing (CEP) deals with detecting real-time situations, represented as event patterns, from among an event cloud. The state-of-the-art CEP systems process events as plain data tuples and are limited to detect precisely defined patterns. Emerging application areas like optimization in smart power grids require CEP to incorporate semantic knowledge of the domain for easier pattern specification, and detect inexact patterns in the presence of uncertainties. In this paper, we present motivating use cases, discuss limitations of existing CEP systems and describe our work towards an Inexact Semantic Complex Event Processing (InSCEP) framework.
---
paper_title: Where to find my next passenger

paper_content:

We present a recommender for taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers' mobility patterns and 2) taxi drivers' pick-up behaviors learned from the GPS trajectories of taxicabs. First, this recommender provides taxi drivers with some locations and the routes to these locations, towards which they are more likely to pick up passengers quickly (during the routes or at these locations) and maximize the profit. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we learn the above knowledge (represented by probabilities) from GPS trajectories of taxis. Then, we feed the knowledge into a probabilistic model which estimates the profit of the candidate locations for a particular driver based on where and when the driver requests for the recommendation. We validate our recommender using historical trajectories generated by over 12,000 taxis during 110 days.
---
paper_title: ParkNet: drive-by sensing of road-side parking statistics

paper_content:

Urban street-parking availability statistics are challenging to obtain in real-time but would greatly benefit society by reducing traffic congestion. In this paper we present the design, implementation and evaluation of ParkNet, a mobile system comprising vehicles that collect parking space occupancy information while driving by. Each ParkNet vehicle is equipped with a GPS receiver and a passenger-side-facing ultrasonic range-finder to determine parking spot occupancy. The data is aggregated at a central server, which builds a real-time map of parking availability and could provide this information to clients that query the system in search of parking. Creating a spot-accurate map of parking availability challenges GPS location accuracy limits. To address this need, we have devised an environmental fingerprinting approach to achieve improved location accuracy. Based on 500 miles of road-side parking data collected over 2 months, we found that parking spot counts are 95% accurate and occupancy maps can achieve over 90% accuracy. Finally, we quantify the amount of sensors needed to provide adequate coverage in a city. Using extensive GPS traces from over 500 San Francisco taxicabs, we show that if ParkNet were deployed in city taxicabs, the resulting mobile sensors would provide adequate coverage and be more cost-effective by an estimated factor of roughly 10-15 when compared to a sensor network with a dedicated sensor at every parking space, as is currently being tested in San Francisco.
---
paper_title: PipeProbe: a mobile sensor droplet for mapping hidden pipeline

paper_content:

This paper presents PipeProbe, a mobile sensor system for determining the spatial topology of hidden water pipelines behind walls. PipeProbe works by dropping a tiny wireless sensor capsule into the source of the water pipelines. As the PipeProbe capsule traverses the pipelines, it gathers and transmits pressure and angular velocity readings. Through spatio-temporal analysis on the sensor readings, our algorithm locates all turning points in the pipelines and maps their 3D spatial topology. We evaluated the PipeProbe system by developing a prototype and using data collected in our experimental testbed. Results show that the PipeProbe system successfully located and estimated 90% of all pipe tube lengths within 8-cm accuracy on average tube lengths of 76 cm. PipeProbe also successfully located 90% of all turning points within 15-cm accuracy on average length paths of 335cm.
---
paper_title: AutoWitness: Locating and tracking stolen property while tolerating GPS and radio outages

paper_content:

We present AutoWitness, a system to deter, detect, and track personal property theft, improve historically dismal stolen property recovery rates, and disrupt stolen property distribution networks. A property owner embeds a small tag inside the asset to be protected, where the tag lies dormant until it detects vehicular movement. Once moved, the tag uses inertial sensor-based dead reckoning to estimate position changes, but to reduce integration errors, the relative position is reset whenever the sensors indicate the vehicle has stopped. The sequence of movements, stops, and turns are logged in compact form and eventually transferred to a server using a cellular modem after both sufficient time has passed (to avoid detection) and RF power is detectable (hinting cellular access may be available). Eventually, the trajectory data are sent to a server which attempts to match a path to the observations. The algorithm uses a Hidden Markov Model of city streets and Viterbi decoding to estimate the most likely path. The proposed design leverages low-power radios and inertial sensors, is immune to intransit cloaking, and supports post hoc path reconstruction. Our prototype demonstrates technical viability of the design; the volume market forces driving machine-to-machine communications will soon make the design economically viable.
---
paper_title: Suelo: human-assisted sensing for exploratory soil monitoring studies

paper_content:

Soil contains vast ecosystems that play a key role in the Earth's water and nutrient cycles, but scientists cannot currently collect the high-resolution data required to fully understand them. In this paper, we present Suelo, an embedded networked sensing system designed for soil monitoring. An important challenge for Suelo is that many soil sensors are inherently fragile and often produce invalid or uncalibrated data. Therefore Suelo is an assisted sensing system: it actively requests the help of a human when necessary to validate, calibrate, repair, or replace sensors. This approach allows us to use available sensors without sacrificing data integrity, while minimizing the human resources required. We tested our system in multiple real soil monitoring deployments and demonstrate that, using human assistance, Suelo produced 91% fewer false negatives and false positives than common fault detection solutions on these datasets.
---
paper_title: Monitoring residential noise for prospective home owners and renters

paper_content:

Residential noise is a leading cause of neighborhood dissatisfaction but is difficult to quantify for it varies in intensity and spectra over time. We have developed a noise model and data representation techniques that prospective homeowners and renters can use to provide quantitative and qualitative answers to the question, "is this a quiet neighborhood?" Residential noise is modeled as an ambient background punctuated by transient events. The quantitative noise model extracts noise features that are sent as SMS text messages. A device that implements the noise model has been build, calibrated and verified. The qualitative impact of sound is subjectively assessed by providing one-minute audio summaries composed of twenty 3-second sound segments that represent the loudest noise events occurring in a 24 hour sampling period. The usefulness and desirability of the noise pollution monitoring service is confirmed with pre- and post-use surveys.
---
paper_title: Validated caloric expenditure estimation using a single body-worn sensor

paper_content:

In 2007, approximately 30% of US adults were obese, with related health care costs exceeding 100 billion dollars. Clearly, the obesity epidemic represents a growing societal concern. One challenge in weight control is the difficulty of tracking food calories consumed and calories expended by activity. This paper presents a system for automatic monitoring of calories expended using a single body-worn accelerometer. Our system uses activity inference combined with signal analysis to estimate calories expended in real-time using regression formulas developed by the American College of Sports Medicine. To validate our system, we have collected data from 51 subjects in a laboratory setting using a treadmill and a more natural field test. Actual caloric expenditure was determined using the medical "gold standard" measurement, of oxygen consumption. We are able to achieve 89% accuracy with lab data and 79% with field data -- both high enough to be useful in practice.
---
paper_title: Where to find my next passenger

paper_content:

We present a recommender for taxi drivers and people expecting to take a taxi, using the knowledge of 1) passengers' mobility patterns and 2) taxi drivers' pick-up behaviors learned from the GPS trajectories of taxicabs. First, this recommender provides taxi drivers with some locations and the routes to these locations, towards which they are more likely to pick up passengers quickly (during the routes or at these locations) and maximize the profit. Second, it recommends people with some locations (within a walking distance) where they can easily find vacant taxis. In our method, we learn the above knowledge (represented by probabilities) from GPS trajectories of taxis. Then, we feed the knowledge into a probabilistic model which estimates the profit of the candidate locations for a particular driver based on where and when the driver requests for the recommendation. We validate our recommender using historical trajectories generated by over 12,000 taxis during 110 days.
---
paper_title: Adding GPS-Control to Traditional Thermostats: An Exploration of Potential Energy Savings and Design Challenges

paper_content:

Although manual and programmable home thermostats can save energy when used properly, studies have shown that over 40% of U.S. homes may not use energy-saving temperature setbacks when homes are unoccupied. We propose a system for augmenting these thermostats using just-in-time heating and cooling based on travel-to-home distance obtained from location-aware mobile phones. Analyzing GPS travel data from 8 participants (8-12 weeks each) and heating and cooling characteristics from 5 homes, we report results of running computer simulations estimating potential energy savings from such a device. Using a GPS-enabled thermostat might lead to savings of as much as 7% for some households that do not regularly use the temperature setback afforded by manual and programmable thermostats. Significantly, these savings could be obtained without requiring any change in occupant behavior or comfort level, and the technology could be implemented affordably by exploiting the ubiquity of mobile phones. Additional savings may be possible with modest context-sensitive prompting. We report on design considerations identified during a pilot test of a fully-functional implementation of the system.
---
paper_title: GreenGPS: a participatory sensing fuel-efficient maps application

paper_content:

This paper develops a navigation service, called GreenGPS, that uses participatory sensing data to map fuel consumption on city streets, allowing drivers to find the most fuel efficient routes for their vehicles between arbitrary end-points. The service exploits measurements of vehicular fuel consumption sensors, available via the OBD-II interface standardized in all vehicles sold in the US since 1996. The interface gives access to most gauges and engine instrumentation. The most fuel-efficient route does not always coincide with the shortest or fastest routes, and may be a function of vehicle type. Our experimental study shows that a participatory sensing system can influence routing decisions of individual users and also answers two questions related to the viability of the new service. First, can it survive conditions of sparse deployment? Second, how much fuel can it save? A challenge in participatory sensing is to generalize from sparse sampling of high-dimensional spaces to produce compact descriptions of complex phenomena. We illustrate this by developing models that can predict fuel consumption of a set of sixteen different cars on the streets of the city of Urbana-Champaign. We provide experimental results from data collection suggesting that a 1% average prediction error is attainable and that an average 10% savings in fuel can be achieved by choosing the right route.
---
paper_title: Tiny web services: design and implementation of interoperable and evolvable sensor networks

paper_content:

We present a web service based approach to enable an evolutionary sensornet system where additional sensor nodes may be added after the initial deployment. The functionality and data provided by the new nodes is exposed in a structured manner, so that multiple applications may access them. The result is a highly inter-operable system where multiple applications can share a common evolving sensor substrate. A key challenge in using web services on resource constrained sensor nodes is the energy and bandwidth overhead of the structured data formats used in web services. Our work provides a detailed evaluation of the overheads and presents an implementation on a representative sensor platform with 48k of ROM, 10k of RAM and a 802.15.4 radio. We identify design choices that optimize the web service operation on resource constrained sensor nodes, including support for low latency messaging and sleep modes, quantifying trade-offs between the design generality and resource efficiency. We also prototyped an example application, for home energy management, demonstrating how evolutionary sensor networks can be supported with our approach.
---
paper_title: Product-mediated communication through digital object memories in heterogeneous value chains

paper_content:

Industrial production and supply chains face an increased demand for mass-customization and tightening regulations for the traceability of goods, leading to higher requirements concerning flexibility, adaptability and transparency of processes. Technologies for the “Internet of Things” such as smart products and semantic representations pave their way into future factories and supply chains to fulfill these challenging market demands. In this paper a backend-independent approach for information exchange in open-loop processes based on so-called digital object memories (DOM) is presented. By storing order-related data via smart labels on the item, relevant life cycle information is attached to the product itself. This way, information handover via several stages of the value chain with potentially different stakeholders including manufacturer, distributor, retailer, and end customer has been realized. To summarize first best-practice experiences regarding memory structure and content, a prototype implementation based on a scenario of processing dietary supplements in an adaptive process is illustrated.
---
paper_title: Modeling Object Flows from Distributed and Federated RFID Data Streams for Efficient Tracking and Tracing

paper_content:

In the emerging environment of the Internet of things (IoT), through the connection of billions of radio frequency identification (RFID) tags and sensors to the Internet, applications will generate an unprecedented number of transactions and amount of data that require novel approaches in RFID data stream processing and management. Unfortunately, it is difficult to maintain a distributed model without a shared directory or structured index. In this paper, we propose a fully distributed model for federated RFID data streams. This model combines two techniques, namely, tilted time frame and histogram to represent the patterns of object flows. Our model is efficient in space and can be stored in main memory. The model is built on top of an unstructured P2P overlay. To reduce the overhead of distributed data acquisition, we further propose several algorithms that use a statistically minimum number of network calls to maintain the model. The scalability and efficiency of the proposed model are demonstrated through an extensive set of experiments.
---
paper_title: Detecting inconsistencies in distributed data

paper_content:

One of the central problems for data quality is inconsistency detection. Given a database D and a set Σ of dependencies as data quality rules, we want to identify tuples in D that violate some rules in Σ. When D is a centralized database, there have been effective SQL-based techniques for finding violations. It is, however, far more challenging when data in D is distributed, in which inconsistency detection often necessarily requires shipping data from one site to another. This paper develops techniques for detecting violations of conditional functional dependencies (CFDs) in relations that are fragmented and distributed across different sites. (1) We formulate the detection problem in various distributed settings as optimization problems, measured by either network traffic or response time. (2)We show that it is beyond reach in practice to find optimal detection methods: the detection problem is NP-complete when the data is partitioned either horizontally or vertically, and when we aim to minimize either data shipment or response time. (3) For data that is horizontally partitioned, we provide several algorithms to find violations of a set of CFDs, leveraging the structure of CFDs to reduce data shipment or increase parallelism. (4) We verify experimentally that our algorithms are scalable on large relations and complex CFDs. (5) For data that is vertically partitioned, we provide a characterization for CFDs to be checked locally without requiring data shipment, in terms of dependency preservation. We show that it is intractable to minimally refine a partition and make it dependency preserving.
---
paper_title: Sense the physical, walkthrough the virtual, manage the co (existing) spaces: a database perspective

paper_content:

In a co-space environment, the physical space and the virtual space co-exist, and interact simultaneously. While the physical space is virtually enhanced with information, the virtual space is continuously refreshed with real-time, real-world information. To allow users to process and manipulate information seamlessly between the real and digital spaces, novel technologies must be developed. These include smart interfaces, new augmented realities, efficient storage and data management and dissemination techniques. In this paper, we first discuss some promising co-space applications. These applications offer experiences and opportunities that neither of the spaces can realize on its own. We then argue that the database community has much to offer to this field. Finally, we present several challenges that we, as a community, can contribute towards managing the co-space.
---
paper_title: Research Directions in Database Architectures for the Internet of Things: A Communication of the First International Workshop on Database Architectures for the Internet of Things (DAIT 2009)

paper_content:

This paper is a record of the First International Workshop on Database Architectures for the Internet of Things. The Internet of Things refers to the future internet which will contain trillions of nodes representing various objects from small ubiquitous sensor devices and handhelds to large web servers and supercomputer clusters. The workshop investigated a number of areas appertaining to data management in the Internet of Things from storage structures, through database management methods, to service-oriented architectures and new approaches to information search. Running orthogonal to these layers the matter of security was also considered. Taking a philosophical viewpoint our whole current framework for understanding data management may be ill-equipped to meet the challenges of the Internet of Things. The workshop gave participants the chance to discuss these matters, exchange ideas and explore future collaborations.
---
paper_title: DTT: A Distributed Trust Toolkit for pervasive systems

paper_content:

Effective security mechanisms are essential to the widespread deployment of pervasive systems. Much of the research focus on security in pervasive computing has revolved around distributed trust management. While such mechanisms are effective in specific environments, there is no generic framework for deploying and extending these mechanisms over a variety of pervasive systems. We present the design and implementation of a novel framework called Distributed Trust Toolkit (DTT), for implementing and evaluating trust mechanisms in pervasive systems. The DTT facilitates the extension and adaptation of trust mechanisms by abstracting trust mechanisms into interchangeable components. Furthermore, the DTT provides a set of tools and interfaces to ease implementation of trust mechanisms and facilitate their execution on a variety of platforms and networks. In addition to the adaptability and extensibility provided by this design, we demonstrate through simulation that use of DTT improves utilization of resources and enhances performance of existing trust mechanisms in pervasive systems. We are currently developing an implementation of the DTT that can be easily deployed in pervasive environments.
---
paper_title: Database researchers: plumbers or thinkers?

paper_content:

DB researchers have traditionally focused on engine-centered issues such as indexing, query processing, and transactions. Data mining has broadened the community's viewpoint towards algorithmic and statistical issues. However, DB research has always had a tendency to shy away from seemingly elusive long-term challenges with AI flavor. On the other hand, the current explosion of digital content in enterprises and the Internet, is mostly caused by user-created information like text, tags, photos, videos, and not by seeing more well-designed databases of the traditional kind. In this situation, I question the traditional skepticism of DB researchers towards "AI-complete" problems and the DB community's reluctance to embark on seemingly non-DB-ish grand challenges. Big questions that I see as great opportunities also for DB research include: 1) automatic extraction of relational facts from natural-language text and multimodal contexts [4, 6, 21], 2) automatic disambiguation of named-entity mentions and general phrases in text and speech [10, 11], 3) large-scale gathering of factual-knowledge candidates and their reconciliation into comprehensive knowledge bases [1, 2, 8, 13, 19], 4) reasoning on uncertain hypotheses, for knowledge discovery and semantic search [9, 14, 16, 17, 20], 5) deep and real-time question answering, e.g., to enable computers to win quiz game shows [7], 6) machine-reading of scientific publications and fictional literature, to enable corpus-wide analyses and enable researchers in science and humanities to develop hypotheses and quickly focus on the most relevant issues [3, 5]. I believe that successfully tackling these topics requires efficient data-centric algorithms, scalable methods and architectures, and system-level thinking - virtues that are richly available in the DB research community. Moreover, I would encourage our community to look across the fence and get more engaged on the exciting challenges outside the traditionally narrow boundaries of the DB realm. I will illustrate these points by examples from my own research on knowledge management [12, 15, 18, 19]. Breakthroughs will require long-term stamina. In the meantime, steady incremental progress is better than not embarking on these important problems at all.
---
paper_title: Building the Internet of Things Using RFID: The RFID Ecosystem Experience

paper_content:

At the University of Washington, the RFID ecosystem creates a microcosm for the Internet of Things. The authors developed a suite of Web-based, user-level tools and applications designed to empower users by facilitating their understanding, management, and control of personal RFID data and privacy settings. They deployed these applications in the RFID ecosystem and conducted a four-week user study to measure trends in adoption and utilization of the tools and applications as well as users' qualitative reactions.
---
paper_title: Exploring privacy concerns about personal sensing

paper_content:

More and more personal devices such as mobile phones and multimedia players use embedded sensing. This means that people are wearing and carrying devices capable of sensing details about them such as their activity, location, and environment. In this paper, we explore privacy concerns about such personal sensing through interviews with 24 participants who took part in a three month study that used personal sensing to detect their physical activities. Our results show that concerns often depended on what was being recorded, the context in which participants worked and lived and thus would be sensed, and the value they perceived would be provided. We suggest ways in which personal sensing can be made more privacy-sensitive to address these concerns.
---

Title: When Things Matter: A Survey on Data-Centric Internet of Things

Section 1: Introduction
Description 1: Introduce the concept of the Internet of Things (IoT) and its evolution from the traditional Internet, focusing on the connection and communication of everyday objects.

Section 2: IoT Data Taxonomy
Description 2: Identify the intrinsic characteristics of IoT data and classify them into categories such as Data Generation, Data Quality, and Data Interoperability.

Section 3: Data Generation
Description 3: Discuss various aspects of data generation in IoT, including velocity, scalability, dynamics, and heterogeneity.

Section 4: Data Quality
Description 4: Examine the quality attributes of IoT data, such as uncertainty, redundancy, ambiguity, and inconsistency.

Section 5: Data Interoperability
Description 5: Explore the aspects of data interoperability in IoT, focusing on issues like incompleteness and semantics.

Section 6: Data Streams
Description 6: Describe data stream processing in IoT, including general data stream processing, RFID data stream processing, and RDF triple stream processing.

Section 7: Data Storage Models
Description 7: Discuss the requirements and techniques for data storage in IoT, focusing on new architectures, large-scale storage in distributed environments, and storage on resource-constrained devices.

Section 8: Search Techniques
Description 8: Review techniques for searching things in the IoT environment, including Deep Web, Semantic Web, web search, real-time web search, and search of things in IoT.

Section 9: Complex Event Processing
Description 9: Describe complex event processing (CEP) techniques in the context of IoT, focusing on event pattern matching and semantic complex event processing.

Section 10: Potential IoT Applications
Description 10: Discuss ongoing and potential IoT applications across various domains such as smart cities, environment monitoring, health, energy, and business.

Section 11: Open Issues
Description 11: Identify key challenges and research directions in the development and application of IoT, with a focus on data quality, transaction handling, and semantic enrichment.

Section 12: Summary
Description 12: Summarize the key points of the survey, highlighting the significance of IoT from a data-centric perspective and encouraging further research efforts.