Showing papers on "Participatory sensing published in 2010"

A survey of mobile phone sensing

Abstract: Mobile phones or smartphones are rapidly becoming the central computer and communication device in people's lives. Application delivery channels such as the Apple AppStore are transforming mobile phones into App Phones, capable of downloading a myriad of applications in an instant. Importantly, today's smartphones are programmable and come with a growing set of cheap powerful embedded sensors, such as an accelerometer, digital compass, gyroscope, GPS, microphone, and camera, which are enabling the emergence of personal, group, and communityscale sensing applications. We believe that sensor-equipped mobile phones will revolutionize many sectors of our economy, including business, healthcare, social networks, environmental monitoring, and transportation. In this article we survey existing mobile phone sensing algorithms, applications, and systems. We discuss the emerging sensing paradigms, and formulate an architectural framework for discussing a number of the open issues and challenges emerging in the new area of mobile phone sensing research.

Ear-phone: an end-to-end participatory urban noise mapping system

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

Recruitment framework for participatory sensing data collections

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

Sell your experiences: a market mechanism based incentive for participatory sensing

Abstract: This paper studies economic models of user participation incentive in participatory sensing applications. User participation is the most important element in participatory sensing applications for providing adequate level of service quality. However, incentive mechanism and its economic model for user participation have never been addressed so far in this research domain. In order to stimulate user participation, we design and evaluate a novel Reverse Auction based Dynamic Price (RADP) incentive mechanism, where users can sell their sensing data to a service provider with users' claimed bid prices. The proposed incentive mechanism focuses on minimizing and stabilizing incentive cost while maintaining adequate number of participants by preventing users from dropping out of participatory sensing applications. Compared with a Random Selection with Fixed Price (RSFP) incentive mechanism, the proposed mechanism not only reduces the incentive cost for retaining same number of participants by more than 60% but also improves the fairness of incentive distribution and social welfare. More importantly, RADP can remove burden of accurate pricing for user sensing data, the most difficult step in RSFP.

GreenGPS: a participatory sensing fuel-efficient maps application

Abstract: 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.

PRISM: platform for remote sensing using smartphones

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

Participatory noise pollution monitoring using mobile phones

Abstract: Noise pollution is a major problem in cities around the world The current methods to assess it neglect to represent the real exposure experienced by the citizens themselves, and therefore could lead to wrong conclusions and a biased representations In this paper we present a novel approach to monitor noise pollution involving the general public Using their mobile phones as noise sensors, we provide a low cost solution for the citizens to measure their personal exposure to noise in their everyday environment and participate in the creation of collective noise maps by sharing their geo-localized and annotated measurements with the community Our prototype, called NoiseTube, can be found online [1]

Examining micro-payments for participatory sensing data collections

Abstract: The rapid adoption of mobile devices that are able to capture and transmit a wide variety of sensing modalities (media and location) has enabled a new data collection paradigm - participatory sensing. Participatory sensing initiatives organize individuals to gather sensed information using mobile devices through cooperative data collection. A major factor in the success of these data collection projects is sustained, high quality participation. However, since data capture requires a time and energy commitment from individuals, incentives are often introduced to motivate participants. In this work, we investigate the use of micro-payments as an incentive model. We define a set of metrics that can be used to evaluate the effectiveness of incentives and report on findings from a pilot study using various micro-payment schemes in a university campus sustainability initiative.

Toward trustworthy mobile sensing

Abstract: Commodity mobile devices have been utilized as sensor nodes in a variety of domains, including citizen journalism, mobile social services, and domestic eldercare. In each of these domains, data integrity and device-owners' privacy are first-class concerns, but current approaches to secure sensing fail to balance these properties. External signing infrastructure cannot attest to the values generated by a device's sensing hardware, while trusted sensing hardware does not allow users to securely reduce the fidelity of readings in order to preserve their privacy. In this paper we examine the challenges posed by the potentially conflicting goals of data integrity and user privacy and propose a trustworthy mobile sensing platform which leverages inexpensive commodity Trusted Platform Module (TPM) hardware.

Are you contributing trustworthy data?: the case for a reputation system in participatory sensing

Abstract: Participatory sensing is a revolutionary new paradigm in which volunteers collect and share information from their local environment using mobile phones. The inherent openness of this platform makes it easy to contribute corrupted data. This paper proposes a novel reputation system that employs the Gompertz function for computing device reputation score as a reflection of the trustworthiness of the contributed data. We implement this system in the context of a participatory noise monitoring application and conduct extensive real-world experiments using Apple iPhones. Experimental results demonstrate that our scheme achieves three-fold improvement in comparison with the state-of-the-art Beta reputation scheme.

I am a sensor, and I approve this message

Abstract: Despite the popularity of adding sensors to mobile devices, the readings provided by these sensors cannot be trusted. Users can fabricate sensor readings with relatively little effort. This lack of trust discourages the emergence of applications where users have an incentive to lie about their sensor readings, such as falsifying a location or altering a photo taken by the camera.This paper presents a broad range of applications that would benefit from the deployment of trusted sensors, from participatory sensing to monitoring energy consumption. We present two design alternatives for making sensor readings trustworthy. Although both designs rely on the presence of a trusted platform module (TPM), they trade-off security guarantees for hardware requirements. While our first design is less secure, it requires no additional hardware beyond a TPM, unlike our second design. Finally, we present the privacy issues arising from the deployment of trusted sensors and we discuss protocols that can overcome them.

Crowdsourcing of Pollution Data using Smartphones

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

Personal data vaults: a locus of control for personal data streams

Abstract: The increasing ubiquity of the mobile phone is creating many opportunities for personal context sensing, and will result in massive databases of individuals' sensitive information incorporating locations, movements, images, text annotations, and even health data. In existing system architectures, users upload their raw (unprocessed or filtered) data streams directly to content-service providers and have little control over their data once they "opt-in". We present Personal Data Vaults (PDVs), a privacy architecture in which individuals retain ownership of their data. Data are routinely filtered before being shared with content-service providers, and users or data custodian services can participate in making controlled data-sharing decisions. Introducing a PDV gives users flexible and granular access control over data. To reduce the burden on users and improve usability, we explore three mechanisms for managing data policies: Granular ACL, Trace-audit and Rule Recommender. We have implemented a proof-of-concept PDV and evaluated it using real data traces collected from two personal participatory sensing applications.

Participatory Sensing: Applications and Architecture

Abstract: Participatory sensing is the process whereby individuals and communities use ever-more-capable mobile phones and cloud services to collect and analyze systematic data for use in discovery. The convergence of technology and analytical innovation with a citizenry that is increasingly comfortable using mobile phones and online social networking sets the stage for this technology to dramatically impact many aspects of our daily lives.Participatory sensing can serve as a powerful "make a case" technology to support advocacy and civic engagement. It can provide a framework in which citizens can bring to light a civic bottleneck, hazard, personal-safety concern, cultural asset, or other data relevant to urban and natural-resources planning and services, all using data that are systematic and can be validated (http://whatsinvasive.com.) The same systems can be used as tools for sustainability. For example, individuals and communities can explore their transportation and consumption habits and corporations can promote more sustainable practices among employees (http://peir.cens.ucla.edu and http://biketastic.com.)

Participatory sensing: applications and architecture [Internet Predictions]

Abstract: Participatory sensing is the process whereby individuals and communities use ever more capable mobile phones and cloud services to collect and analyze systematic data for use in discovery. The convergence of technology and analytical innovation with a citizenry that is increasingly comfortable using mobile phones and online social networking sets the stage for this technology to dramatically impact many aspects of daily lives. Ubiquitous data capture, leveraged data processing, and personal data vault are the essential components for these emerging systems. The architecture, usage models,and application of participatory sensing were discussed in this paper.

Common sense community: scaffolding mobile sensing and analysis for novice users

Abstract: As sensing technologies become increasingly distributed and democratized, citizens and novice users are becoming responsible for the kinds of data collection and analysis that have traditionally been the purview of professional scientists and analysts. Leveraging this citizen engagement effectively, however, requires not only tools for sensing and data collection but also mechanisms for understanding and utilizing input from both novice and expert stakeholders. When successful, this process can result in actionable findings that leverage and engage community members and build on their experiences and observations. We explored this process of knowledge production through several dozen interviews with novice community members, scientists, and regulators as part of the design of a mobile air quality monitoring system. From these interviews, we derived design principles and a framework for describing data collection and knowledge generation in citizen science settings, culminating in the user-centered design of a system for community analysis of air quality data. Unlike prior systems, ours breaks analysis tasks into discrete mini-applications designed to facilitate and scaffold novice contributions. An evaluation we conducted with community members in an area with air quality concerns indicates that these mini-applications help participants identify relevant phenomena and generate local knowledge contributions.

Intentional networking: opportunistic exploitation of mobile network diversity

Abstract: Mobile devices face a diverse and dynamic set of networking options. Using those options to the fullest requires knowledge of application intent. This paper describes Intentional Networking, a simple but powerful mechanism for handling network diversity. Applications supply a declarative label for network transmissions, and the system matches transmissions to the most appropriate network. The system may also defer and re-order opportunistic transmissions subject to application-supplied mutual exclusion and ordering constraints. We have modified three applications to use Intentional Networking: BlueFS, a distributed file system for pervasive computing, Mozilla's Thunderbird e-mail client, and a vehicular participatory sensing application. We evaluated the performance of these applications using measurements obtained by driving a vehicle through WiFi and cellular 3G network coverage. Compared to an idealized solution that makes optimal use of all aggregated available networks but that lacks knowledge of application intent, Intentional Networking improves the latency of interactive messages from 48% to 13x, while adding no more than 7% throughput overhead.

Participatory sensing in public spaces: activating urban surfaces with sensor probes

Abstract: Recent convergence between low-cost technology, artform and political discourse presents a new design space for enabling public participation and expression. We explore non-experts' use of place-based, modular sensors to activate, author and provoke urban landscapes. Our work with communities of bicyclists, students, parents, and homeless people suggests design opportunities for merging grassroots data collection with public expressions and activism. Members of each community were given probes that represent the measurement of exhaust, smog, pathogens, chemicals, noise or dust, and asked to engage with them as fully functional sensors over the course of one week. Our findings offer insights into participation, environmental sensing, and data sharing within and across four different communities, revealing design implications for future sensing systems as instruments of social currency and political change.

G-Sense: a scalable architecture for global sensing and monitoring

Abstract: The pervasiveness of cellular phones combined with Internet connectivity, GPS embedded chips, location information, and integrated sensors provide an excellent platform to collect data about the individual and its surrounding environment. As a result, new applications have recently appeared to address large-scale societal problems as well as improve the quality of life of the individual. However, these new applications, recently called location-based services, participatory sensing, and human-centric sensing, bring many new challenges, one of them being the management of the huge amount of traffic (data) they generate. This article presents G-Sense, for Global-Sense, an architecture that integrates mobile and static wireless sensor networks in support of location-based services, participatory sensing, and human-centric sensing applications. G-Sense includes specific mechanisms to control the amount of data generated by these applications while meeting the application requirements. Furthermore, it creates a network of servers organized in a peer-to-peer architecture to address scalability and reliability issues. An example prototype application is presented along with some basic results and open research issues.

Privacy-aware regression modeling of participatory sensing data

Abstract: Many participatory sensing applications use data collected by participants to construct a public model of a system or phenomenon. For example, a health application might compute a model relating exercise and diet to amount of weight loss. While the ultimately computed model could be public, the individual input and output data traces used to construct it may be private data of participants (e.g., their individual food intake, lifestyle choices, and resulting weight). This paper proposes and experimentally studies a technique that attempts to keep such input and output data traces private, while allowing accurate model construction. This is significantly different from perturbation-based techniques in that no noise is added. The main contribution of the paper is to show a certain data transformation at the client side that helps keeping the client data private while not introducing any additional error to model construction. We particularly focus on linear regression models which are widely used in participatory sensing applications. We use the data set from a map-based participatory sensing service to evaluate our scheme. The service in question is a green navigation service that constructs regression models from participant data to predict the fuel consumption of vehicles on road segments. We evaluate our proposed mechanism by providing empirical evidence that: i) an individual data trace is generally hard to reconstruct with any reasonable accuracy, and ii) the regression model constructed using the transformed traces has a much smaller error than one based on additive data-perturbation schemes.

Privacy-preserving reconstruction of multidimensional data maps in vehicular participatory sensing

Abstract: The proliferation of sensors in devices of frequent use, such as mobile phones, offers unprecedented opportunities for forming self-selected communities around shared sensory data pools that enable community specific applications of mutual interest. Such applications have recently been termed participatory sensing. An important category of participatory sensing applications is one that construct maps of different phenomena (e.g., traffic speed, pollution) using vehicular participatory sensing. An example is sharing data from GPS-enabled cell-phones to map traffic or noise patterns. Concerns with data privacy are a key impediment to the proliferation of such applications. This paper presents theoretical foundations, a system implementation, and an experimental evaluation of a perturbation-based mechanism for ensuring privacy of location-tagged participatory sensing data while allowing correct reconstruction of community statistics of interest (computed from shared perturbed data). The system is applied to construct accurate traffic speed maps in a small campus town from shared GPS data of participating vehicles, where the individual vehicles are allowed to “lie” about their actual location and speed at all times. An extensive evaluation demonstrates the efficacy of the approach in concealing multi-dimensional, correlated, time-series data while allowing for accurate reconstruction of spatial statistics.

I am a sensor, and I approve this message

Abstract: Despite the popularity of adding sensors to mobile devices, the readings provided by these sensors cannot be trusted. Users can fabricate sensor readings with relatively little effort. This lack of trust discourages the emergence of applications where users have an incentive to lie about their sensor readings, such as falsifying a location or altering a photo taken by the camera.This paper presents a broad range of applications that would benefit from the deployment of trusted sensors, from participatory sensing to monitoring energy consumption. We present two design alternatives for making sensor readings trustworthy. Although both designs rely on the presence of a trusted platform module (TPM), they trade-off security guarantees for hardware requirements. While our first design is less secure, it requires no additional hardware beyond a TPM, unlike our second design. Finally, we present the privacy issues arising from the deployment of trusted sensors and we discuss protocols that can overcome them.

Participatory Sensing: Building Empowering Surveillance

Abstract: Mobile phones could become the largest surveillance system on the planet. These ubiquitous, networked devices can currently sense and upload data such as images, sound, location, and motion using on-board cameras, microphones, GPS, and accelerometers. And they can be triggered and used by billions of individuals around the world. But the emergent, wide-scale sensing systems that phones support pose a number of questions. Who will control the necessary infrastructure for data storage, analysis, sharing, and retention? And to what purposes will such systems be deployed? This paper explores whether these questions can be answered in ways that promote empowering surveillance: large-scale data collection used by individuals and communities to improve their quality of life and increase their power relative to corporations and governments. Researchers in academic and industry laboratories around the world are currently coordinating mobile phone networks for purposes that expand the definition of surveillance. Technology movements, variously called personal sensing, urban sensingor participatory sensing, have emerged within the areas of social computing and urban computing. These research programs endeavor to make ubiquitous devices such as phones a platform for coordinated investigation of human activity. Researchers are exploring ways to introduce these technologies into the public realm, a move that anticipates sensing by people across the world. This paper uses ethnographic data collected in a sensing development laboratory to illuminate possibilities that participatory sensing holds for equitable use, meaningful community participation, and empowerment. Analyzing the motivations and values embedded within the design process and resulting technologies reveals ways in which participatory sensing builds tools for empowering surveillance and responds to the many ethical challenges these new technologies raise.

Privacy assurance in mobile sensing networks: Go beyond trusted servers

Abstract: Mobile devices are becoming the largest sensor network around the world. They could be used to collect a large amount of data with little effort and cost which is leading to a promising future for participatory sensing networks or urban sensing. However, privacy concerns of the mobile users are the major inhibitors hindering massive participation. This paper proposes a solution to user privacy preserving problem in a participatory sensing network. Each user is considered as a node in a social network and users are connected through friendship links which are represented as edges on the network. Typically, each user contributes to the participatory system by uploading his/her acquired data to a server. Instead of uploading data to the server directly, we devised a Hot-Potato-Privacy-Protection Algorithm (HP3) in which data is sent to one of the friends of the user and the friend will choose another friend to deliver the data to the next hop. Hopping goes on until some user-defined threshold is reached, then the last user uploads the data to the server. Friend selection is random and the number of hops is also random and independent. HP3 ensures that the probability that the server can make a successful attack on the data owner is no better than where n is the number of mobile users in the system. Therefore, HP3 protects location privacy as well as data ownership privacy of mobile users. We simulate our approach on some large scale social networks and report some findings in the paper. Experiments show that our system achieves privacy protection for each user against the server with tolerable communication overhead.

Participatory sensing: applications and architecture

Abstract: Participatory sensing is the process whereby individuals and communities use ever-more-capable mobile phones and cloud services to collect and analyze systematic data for use in discovery. The convergence of technology and analytical innovation with a citizenry that is increasingly comfortable using mobile phones and online social networking sets the stage for this technology to dramatically impact many aspects of our daily lives.Participatory sensing can serve as a powerful "make a case" technology to support advocacy and civic engagement. It can provide a framework in which citizens can bring to light a civic bottleneck, hazard, personal-safety concern, cultural asset, or other data relevant to urban and natural-resources planning and services, all using data that are systematic and can be validated (http://whatsinvasive.com.) The same systems can be used as tools for sustainability. For example, individuals and communities can explore their transportation and consumption habits and corporations can promote more sustainable practices among employees (http://peir.cens.ucla.edu and http://biketastic.com.)

Location-Based Information Systems: Developing Real-Time Tracking Applications

Abstract: Drawing on the authors more than six years of R&D in location-based information systems (LBIS) as well as their participation in defining the Java ME Location API 2.0, Location-Based Information Systems: Developing Real-Time Tracking Applications provides information and examples for creating real-time LBIS based on GPS-enabled cellular phones. Each chapter presents a general real-time tracking system example that can be easily adapted to target any application domain and that can incorporate other sensor data to make the system "participatory sensing" or "human-centric sensing." The book covers all of the components needed to develop an LBIS. It discusses cellular phone programming using the Java ME platform, positioning technologies, databases and spatial databases, communications, client- and server-side data processing, and real-time data visualization via Google Maps and Google Earth. Using freely available software, the authors include many code examples and detailed instructions for building your own system and setting up your entire development environment. Web ResourceA companion website at www.csee.usf.edu/~labrador/LBIS provides additional information and supporting material. It contains all of the software packages and applications used in the text as well as PowerPoint slides and laboratory examples. Although LBIS applications are still in the beginning stages, they have the potential to transform our daily lives, from warning us about possible health problems to monitoring pollution levels around us. Exploring this novel technology, Location-Based Information Systems describes the technical components needed to create location-based services with an emphasis on nonproprietary, freely available solutions that work across different technologies and platforms.

Opportunistic collaboration in participatory sensing environments

Abstract: The proliferation of networked mobile devices that can capture and communicate various kinds of data provides an opportunity to design novel man-machine sensing environments of which this paper considers participatory sensing. To achieve energy efficiency and reduce data redundancy, we propose Aquiba protocol that exploits opportunistic collaboration of pedestrians. Sensing activity is reduced according to the number of available pedestrians in nearby area. The paper investigates the benefit of opportunistic collaboration in large-scale scenarios through simulation studies. To take microscopic interaction of social crowds into consideration, we adapt the social force model and include it as one of three mobility models applied in our studies. Though the simulation results depend on mobility models, they validate the benefit of opportunistic collaboration employed by Aquiba protocol.

An agent-based architecture for participatory sensing platform

Abstract: Participatory sensing is an emerging paradigm that enables people to participate in data collection for various purposes. Since this approach was put forward, there have been many participatory sensing systems developed in areas such as environmental monitoring, urban monitoring, and visit monitoring. However most of such systems focus on the technical infrastructure placing less emphasis on interaction design. This paper addresses key issues in developing information systems for participatory sensing based on scenario analysis and proposes a multi-agent architecture to facilitate interactions in participatory sensing system.