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Salil S Kanhere - One of the best experts on this subject based on the ideXlab platform.

  • A Reputation Framework for Social Participatory Sensing Systems
    Mobile Networks and Applications, 2014
    Co-Authors: Haleh Amintoosi, Salil S Kanhere
    Abstract:

    Social Participatory Sensing is a newly proposed paradigm that tries to address the limitations of Participatory Sensing by leveraging online social networks as an infrastructure. A critical issue in the success of this paradigm is to assure the trustworthiness of contributions provided by participants. In this paper, we propose an application-agnostic reputation framework for social Participatory Sensing systems. Our framework considers both the quality of contribution and the trustworthiness level of participant within the social network. These two aspects are then combined via a fuzzy inference system to arrive at a final trust rating for a contribution. A reputation score is also calculated for each participant as a resultant of the trust ratings assigned to him. We adopt the utilization of PageRank algorithm as the building block for our reputation module. Extensive simulations demonstrate the efficacy of our framework in achieving high overall trust and assigning accurate reputation scores.

  • SEW-ing a Simple Endorsement Web to incentivize trustworthy Participatory Sensing
    2014 Eleventh Annual IEEE International Conference on Sensing Communication and Networking (SECON), 2014
    Co-Authors: Salil S Kanhere
    Abstract:

    Two crucial issues to the success of Participatory Sensing are (a) how to incentivize the large crowd of mobile users to participate and (b) how to ensure the Sensing data to be trustworthy. While they are traditionally being studied separately in the literature, this paper proposes a Simple Endorsement Web (SEW) to address both issues in a synergistic manner. The key idea is (a) introducing a social concept called nepotism into Participatory Sensing, by linking mobile users into a social “web of participants” with endorsement relations, and (b) overlaying this network with investment-like economic implications. The social and economic layers are interleaved to provision and enhance incentives and trustworthiness. We elaborate the social implications of SEW, and analyze the economic implications under a Stackelberg game framework. We derive the optimal design parameter that maximizes the utility of the Sensing campaign organizer, while ensuring participants to strictly have incentive to participate. We also design algorithms for participants to optimally “sew” SEW, namely to manipulate the endorsement links of SEW such that their economic benefits are maximized and social constrains are satisfied. Finally, we provide two numerical examples for an intuitive understanding.

  • a trust based recruitment framework for multi hop social Participatory Sensing
    arXiv: Social and Information Networks, 2013
    Co-Authors: Haleh Amintoosi, Salil S Kanhere
    Abstract:

    The idea of social Participatory Sensing provides a substrate to benefit from friendship relations in recruiting a critical mass of participants willing to attend in a Sensing campaign. However, the selection of suitable participants who are trustable and provide high quality contributions is challenging. In this paper, we propose a recruitment framework for social Participatory Sensing. Our framework leverages multi-hop friendship relations to identify and select suitable and trustworthy participants among friends or friends of friends, and finds the most trustable paths to them. The framework also includes a suggestion component which provides a cluster of suggested friends along with the path to them, which can be further used for recruitment or friendship establishment. Simulation results demonstrate the efficacy of our proposed recruitment framework in terms of selecting a large number of well-suited participants and providing contributions with high overall trust, in comparison with one-hop recruitment architecture.

  • Participatory Sensing crowdsourcing data from mobile smartphones in urban spaces
    International Conference on Distributed Computing and Internet Technology, 2013
    Co-Authors: Salil S Kanhere
    Abstract:

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

  • a trust framework for social Participatory Sensing systems
    International Conference on Mobile and Ubiquitous Systems: Networking and Services, 2012
    Co-Authors: Haleh Amintoosi, Salil S Kanhere
    Abstract:

    The integration of Participatory Sensing with online social networks affords an effective means to generate a critical mass of participants, which is essential for the success of this new and exciting paradigm. An equally important issue is ascertaining the quality of the contributions made by the participants. In this paper, we propose an application-agnostic trust framework for social Participatory Sensing. Our framework not only considers an objective estimate of the quality of the raw readings contributed but also incorporates a measure of trust of the user within the social network. We adopt a fuzzy logic based approach to combine the associated metrics to arrive at a final trust score. Extensive simulations demonstrate the efficacy of our framework.

Tarek Abdelzaher - One of the best experts on this subject based on the ideXlab platform.

  • Participatory Sensing Meets Opportunistic Sharing: Automatic Phone-to-Phone Communication in Vehicles
    IEEE Transactions on Mobile Computing, 2016
    Co-Authors: Xiaoshan Sun, Pan Hui, Tarek Abdelzaher, Hengchang Liu, Wei Zheng, John A. Stankovic
    Abstract:

    This paper explores direct phone-to-phone communication (via WiFi interface) among vehicles to support Participatory Sensing applications. Sensing data usually contains location, speed, and fuel consumption of the car, and has a long time delay between collected and transferred to the server. Direct communication among phones aboard is important in reducing data transfer delay time and sharing Participatory Sensing information in an inexpensive manner. We design a practical and optimized communication mechanism for direct phone-to-phone data transfer among phones aboard that strategically enables phone-to-phone and/or phone-to-WiFiAP communications by optimally toggling the phones between the normal client and the hotspot modes. We take advantage of the WiFi hotspot functionality on smartphones, and hence require neither involvement of participants nor changes to existing wireless infrastructure and protocols. An analytical model is established to optimize toggling between client and hotspot modes for optimal system efficiency. We fully implement this system on off-the-shelf Google Galaxy Nexus and Nexus S phones. Through a 35-vehicle two-month deployment study, as well as simulation experiments using the real-world T-drive 9,211-taxicab dataset, we show that our solution significantly reduces data transfer delay time and maintains over 80 percent system efficiency under varying system parameters.

  • experiences with greengps fuel efficient navigation using Participatory Sensing
    IEEE Transactions on Mobile Computing, 2016
    Co-Authors: Fatemeh Saremi, Hossein Ahmadi, Tarek Abdelzaher, Raghu K Ganti, Omid Fatemieh, Hongyan Wang, Hengchang Liu
    Abstract:

    Participatory Sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context of GreenGPS ; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the Participatory Sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed Participatory Sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in [1] , however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5 over the fastest, 11.2 percent over the shortest, and 8.4 percent over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings.

  • artsense anonymous reputation and trust in Participatory Sensing
    International Conference on Computer Communications, 2013
    Co-Authors: Xinlei Wang, Wei Cheng, Prasant Mohapatra, Tarek Abdelzaher
    Abstract:

    With the proliferation of sensor-embedded mobile computing devices, Participatory Sensing is becoming popular to collect information from and outsource tasks to participating users. These applications deal with a lot of personal information, e.g., users' identities and locations at a specific time. Therefore, we need to pay a deeper attention to privacy and anonymity. However, from a data consumer's point of view, we want to know the source of the Sensing data, i.e., the identity of the sender, in order to evaluate how much the data can be trusted. “Anonymity” and “trust” are two conflicting objectives in Participatory Sensing networks, and there are no existing research efforts which investigated the possibility of achieving both of them at the same time. In this paper, we propose ARTSense, a framework to solve the problem of “trust without identity” in Participatory Sensing networks. Our solution consists of a privacy-preserving provenance model, a data trust assessment scheme and an anonymous reputation management protocol. We have shown that ARTSense achieves the anonymity and security requirements. Validations are done to show that we can capture the trust of information and reputation of participants accurately.

  • efficient 3g budget utilization in mobile Participatory Sensing applications
    International Conference on Computer Communications, 2013
    Co-Authors: Hengchang Liu, Pan Hui, Wei Zheng, Zhiheng Xie, Shiguang Wang, Tarek Abdelzaher
    Abstract:

    This paper explores efficient 3G budget utilization in mobile Participatory Sensing applications. 1 Distinct from previous research work that either rely on limited WiFi access points or assume the availability of unlimited 3G communication capability, we offer a more practical Participatory Sensing system that leverages potential 3G budgets that participants contribute at will, and uses it efficiently customized for the needs of multiple Participatory Sensing applications with heterogeneous sensitivity to environmental changes. We address the challenge that the information of data generation and WiFi encounters is not a priori knowledge, and propose an online decision making algorithm that takes advantage of participants' historical data. We also develop a heuristic algorithm to consume less energy and reduce the storage overhead while maintaining efficient 3G budget utilization. Experimental results from a 30-participant deployment demonstrate that, even when the budget is as small as 2.5% of a popular data plan, these two algorithms achieve higher utility of uploaded data compared to the baseline solution, especially, they increase the utility of received data by 151.4% and 137.8% for those sensitive applications.

  • apollo towards factfinding in Participatory Sensing
    Information Processing in Sensor Networks, 2011
    Co-Authors: Jeff Pasternack, Hossein Ahmadi, Manish Gupta, Yizhou Sun, Tarek Abdelzaher, Jiawei Han, Dan Roth, Boleslaw K Szymanski, Sibel Adali
    Abstract:

    This demonstration presents Apollo, a new sensor information processing tool for uncovering likely facts in noisy Participatory Sensing data1. Participatory Sensing, where users proactively document and share their observations, has received significant attention in recent years as a paradigm for crowd-sourcing observation tasks. However, it poses interesting challenges in assessing confidence in the information received. By borrowing clustering and ranking tools from data mining literature, we show how to group data into sets (or claims), corroborating specific events or observations, then iteratively assess both claim and source credibility, ultimately leading to a ranking of described claims by their like-lihoold of occurrence. Apollo belongs to a category of tools called fact-finders. It is the first fact-finder designed and implemented specifically for Participatory Sensing. Apollo uses Twitter as the underlying engine for sharing Participatory Sensing data. Twitter is widely popular, can be interfaced to cell-phones that share sensor data, and comes with a powerful search API, as well as a publish-subscribe mechanism. We evaluate it using a Participatory Sensing application that collects and posts noisy vehicular traffic data on Twitter, as well as a set of 60,000 (human) tweets collected during the Haiti tsunami and a set of 500,000 tweets collected about Cairo during its recent unrest. Viewers of the demonstration will interact with Apollo for various fact-finding tasks.

Delphine Christin - One of the best experts on this subject based on the ideXlab platform.

  • privacy in mobile Participatory Sensing
    Journal of Systems and Software, 2016
    Co-Authors: Delphine Christin
    Abstract:

    Analysis of privacy implications and threats in Participatory Sensing.Survey of recent privacy mechanisms for Participatory Sensing.Identification of addressed and remaining privacy research challenges. Mobile Participatory Sensing has opened the doors to numerous Sensing scenarios that were unimaginable few years ago. In absence of protection mechanisms, most of these applications may however endanger the privacy of the participants and end users. In this manuscript, we highlight both sources and targets of these threats to privacy and analyze how they are addressed in recent privacy-preserving mechanisms tailored to the characteristics of Participatory Sensing. We further provide an overview of current trends and future research challenges in this area.

  • incognisense an anonymity preserving reputation framework for Participatory Sensing applications
    IEEE International Conference on Pervasive Computing and Communications, 2012
    Co-Authors: Delphine Christin, Christian Rosskopf, Matthias Hollick, Leonardo A. Martucci, Salil S Kanhere
    Abstract:

    Reputation systems rate the contributions to Participatory Sensing campaigns from each user by associating a reputation score. The reputation scores are used to weed out incorrect sensor readings. However, an adversary can deanonmyize the users even when they use pseudonyms by linking the reputation scores associated with multiple contributions. Since the contributed readings are usually annotated with spatiotemporal information, this poses a serious breach of privacy for the users. In this paper, we address this privacy threat by proposing a framework called IncogniSense. Our system utilizes periodic pseudonyms generated using blind signature and relies on reputation transfer between these pseudonyms. The reputation transfer process has an inherent trade-off between anonymity protection and loss in reputation. We investigate by means of extensive simulations several reputation cloaking schemes that address this tradeoff in different ways. Our system is robust against reputation corruption and a prototype implementation demonstrates that the associated overheads are minimal.

  • a survey on privacy in mobile Participatory Sensing applications
    Journal of Systems and Software, 2011
    Co-Authors: Delphine Christin, Salil S Kanhere, Andreas Reinhardt, Matthias Hollick
    Abstract:

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

Daqing Zhang - One of the best experts on this subject based on the ideXlab platform.

  • allocating heterogeneous tasks in Participatory Sensing with diverse participant side factors
    IEEE Transactions on Mobile Computing, 2019
    Co-Authors: Jiangtao Wang, Daqing Zhang, Yasha Wang, Feng Wang, Brian Y Lim, Leye Wang
    Abstract:

    This paper proposes a novel task allocation framework, PSTasker, for Participatory Sensing (PS), which aims to maximize the overall system utility on PS platform by coordinating the allocation of multiple tasks. While existing studies mainly optimize the task allocation from the perspective of the task organizer (e.g., maximizing coverage or minimizing incentive cost), PSTasker further considers diverse factors on the participants’ side, including user work bandwidth, user availability, devices’ sensor configuration, task completion likelihood, and mobility pattern. Furthermore, by considering the heterogeneity in three dimensions (i.e., task, time, and space), it adopts a novel model to measure task Sensing quality and overall system utility. In PSTasker, it first calculates the utlity of a given task allocation plan by jointly fusing different participant-side factors into one unified estimation function, and then employs an iterative greedy process to optimize the task allocation. Extensive evaluations based on real-world mobility traces demonstrate that PSTasker outperforms the baseline methods under various settings.

  • psallocator multi task allocation for Participatory Sensing with Sensing capability constraints
    Conference on Computer Supported Cooperative Work, 2017
    Co-Authors: Jiangtao Wang, Daqing Zhang, Yasha Wang, Feng Wang, Yuanduo He
    Abstract:

    This paper proposes a novel multi-task allocation framework, named PSAllocator, for Participatory Sensing (PS). Different from previous single-task oriented approaches, which select an optimal set of users for each single task independently, PSAllocator attempts to coordinate the allocation of multiple tasks to maximize the overall system utility on a multi-task PS platform. Furthermore, PSAllocator takes the maximum number of Sensing tasks allowed for each participant and the sensor availability of each mobile device into consideration. PSAllocator utilizes a two-phase offline multi-task allocation approach to achieve the near-optimal goal. First, it predicts the participants' connections to cell towers and locations based on historical data from the telecom operator; Then, it converts the multi-task allocation problem into the representation of a bipartite graph, and employs an iterative greedy process to optimize the task allocation. Extensive evaluations based on real-world mobility traces show that PSAllocator outperforms the baseline methods under various settings.

  • fine grained multitask allocation for Participatory Sensing with a shared budget
    IEEE Internet of Things Journal, 2016
    Co-Authors: Jiangtao Wang, Daqing Zhang, Yasha Wang, Leye Wang, Haoyi Xiong, Abdelsalam Helal, Feng Wang
    Abstract:

    For Participatory Sensing, task allocation is a crucial research problem that embodies a tradeoff between Sensing quality and cost. An organizer usually publishes and manages multiple tasks utilizing one shared budget. Allocating multiple tasks to participants, with the objective of maximizing the overall data quality under the shared budget constraint, is an emerging and important research problem. We propose a fine-grained multitask allocation framework (MTPS), which assigns a subset of tasks to each participant in each cycle. Specifically, considering the user burden of switching among varying Sensing tasks, MTPS operates on an attention-compensated incentive model where, in addition to the incentive paid for each specific Sensing task, an extra compensation is paid to each participant if s/he is assigned with more than one task type. Additionally, based on the prediction of the participants’ mobility pattern, MTPS adopts an iterative greedy process to achieve a near-optimal allocation solution. Extensive evaluation based on real-world mobility data shows that our approach outperforms the baseline methods, and theoretical analysis proves that it has a good approximation bound.

  • Mobile crowd Sensing and computing: when Participatory Sensing meets Participatory social media
    IEEE Communications Magazine, 2016
    Co-Authors: Bin Guo, Daqing Zhang, Chao Chen, Alvin Chin
    Abstract:

    With the development of mobile Sensing and mobile social networking techniques, mobile crowd Sensing and computing (MCSC), which leverages heterogeneous crowdsourced data for large-scale Sensing, has become a leading paradigm. Built on top of the Participatory Sensing vision, MCSC has two characteristic features: it leverages heterogeneous crowdsourced data from two data sources: Participatory Sensing and Participatory social media; and it presents the fusion of human and machine intelligence in both the Sensing and computing processes. This article characterizes the unique features and challenges of MCSC. We further present early efforts on MCSC to demonstrate the benefits of aggregating heterogeneous crowdsourced data

  • Mobile crowd Sensing and computing: When Participatory Sensing meets Participatory social media
    IEEE Communications Magazine, 2016
    Co-Authors: Bin Guo, Daqing Zhang, Zhiwen Yu, Alvin Chin
    Abstract:

    With the development of mobile Sensing and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC), which leverages heterogeneous crowdsourced data for large-scale Sensing, has become a leading paradigm. Built on top of the Participatory Sensing vision, MCSC has two characterizing features: (1) it leverages heterogeneous crowdsourced data from two data sources: Participatory Sensing and Participatory social media; and (2) it presents the fusion of human and machine intelligence (HMI) in both the Sensing and computing process. This paper characterizes the unique features and challenges of MCSC. We further present early efforts on MCSC to demonstrate the benefits of aggregating heterogeneous crowdsourced data.

Raghu K Ganti - One of the best experts on this subject based on the ideXlab platform.

  • experiences with greengps fuel efficient navigation using Participatory Sensing
    IEEE Transactions on Mobile Computing, 2016
    Co-Authors: Fatemeh Saremi, Hossein Ahmadi, Tarek Abdelzaher, Raghu K Ganti, Omid Fatemieh, Hongyan Wang, Hengchang Liu
    Abstract:

    Participatory Sensing services based on mobile phones constitute an important growing area of mobile computing. Most services start small and hence are initially sparsely deployed. Unless a mobile service adds value while sparsely deployed, it may not survive conditions of sparse deployment. The paper offers a generic solution to this problem and illustrates this solution in the context of GreenGPS ; a navigation service that allows drivers to find the most fuel-efficient routes customized for their vehicles between arbitrary end-points. Specifically, when the Participatory Sensing service is sparsely deployed, we demonstrate a general framework for generalization from sparse collected data to produce models extending beyond the current data coverage. This generalization allows the mobile service to offer value under broader conditions. GreenGPS uses our developed Participatory Sensing infrastructure and generalization algorithms to perform inexpensive data collection, aggregation, and modeling in an end-to-end automated fashion. The models are subsequently used by our backend engine to predict customized fuel-efficient routes for both members and non-members of the service. GreenGPS is offered as a mobile phone application and can be easily deployed and used by individuals. A preliminary study of our green navigation idea was performed in [1] , however, the effort was focused on a proof-of-concept implementation that involved substantial offline and manual processing. In contrast, the results and conclusions in the current paper are based on a more advanced and accurate model and extensive data from a real-world phone-based implementation and deployment, which enables reliable and automatic end-to-end data collection and route recommendation. The system further benefits from lower cost and easier deployment. To evaluate the green navigation service efficiency, we conducted a user subject study consisting of 22 users driving different vehicles over the course of several months in Urbana-Champaign, IL. The experimental results using the collected data suggest that fuel savings of 21.5 over the fastest, 11.2 percent over the shortest, and 8.4 percent over the Garmin eco routes can be achieved by following GreenGPS green routes. The study confirms that our navigation service can survive conditions of sparse deployment and at the same time achieve accurate fuel predictions and lead to significant fuel savings.

  • privacy aware regression modeling of Participatory Sensing data
    International Conference on Embedded Networked Sensor Systems, 2010
    Co-Authors: Hossein Ahmadi, Tarek Abdelzaher, Nam Pham, Raghu K Ganti, Suman Nath, Jiawei Han
    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.

  • greengps a Participatory Sensing fuel efficient maps application
    International Conference on Mobile Systems Applications and Services, 2010
    Co-Authors: Raghu K Ganti, Hossein Ahmadi, Nam Pham, Saurabh Nangia, Tarek Abdelzaher
    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.

  • privacy preserving reconstruction of multidimensional data maps in vehicular Participatory Sensing
    International Conference on Embedded Wireless Systems and Networks, 2010
    Co-Authors: Nam Pham, Raghu K Ganti, Yusuf Sarwar Uddin, Suman Nath, Tarek Abdelzaher
    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.

  • poolview stream privacy for grassroots Participatory Sensing
    International Conference on Embedded Networked Sensor Systems, 2008
    Co-Authors: Raghu K Ganti, Nam Pham, Yuen Tsai, Tarek Abdelzaher
    Abstract:

    This paper develops mathematical foundations and architectural components for providing privacy guarantees on stream data in grassroots Participatory Sensing applications, where groups of participants use privately-owned sensors to collectively measure aggregate phenomena of mutual interest. Grassroots applications refer to those initiated by members of the community themselves as opposed to by some governing or official entities. The potential lack of a hierarchical trust structure in such applications makes it harder to enforce privacy. To address this problem, we develop a privacy-preserving architecture, called PoolView, that relies on data perturbation on the client-side to ensure individuals' privacy and uses community-wide reconstruction techniques to compute the aggregate information of interest. PoolView allows arbitrary parties to start new services, called pools, to compute new types of aggregate information for their clients. Both the client-side and server-side components of PoolView are implemented and available for download, including the data perturbation and reconstruction components. Two simple Sensing services are developed for illustration; one computes traffic statistics from subscriber GPS data and the other computes weight statistics for a particular diet. Evaluation, using actual data traces collected by the authors, demonstrates the privacy-preserving aggregation functionality in PoolView.