Participatory Sensing Research Articles

Energy-Efficient Collaborative Outdoor Localization for Participatory Sensing.

Location information is a key element of participatory sensing. Many mobile and sensing applications require location information to provide better recommendations, object search and trip planning. However, continuous GPS positioning consumes much energy, which may drain the battery of mobile devices quickly. Although WiFi and cell tower positioning are alternatives, they provide lower accuracy compared to GPS. This paper solves the above problem by proposing a novel localization scheme through the collaboration of multiple mobile devices to reduce energy consumption and provide accurate positioning. Under our scheme, the mobile devices are divided into three groups, namely the broadcaster group, the location information receiver group and the normal participant group. Only the broadcaster group and the normal participant group use their GPS. The location information receiver group, on the other hand, makes use of the locations broadcast by the broadcaster group to estimate their locations. We formulate the broadcaster set selection problem and propose two novel algorithms to minimize the energy consumption in collaborative localization. Simulations with real traces show that our proposed solution can save up to 68% of the energy of all of the participants and provide more accurate locations than WiFi and cellular network positioning.

Incentive Mechanisms for Participatory Sensing

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

PURE: Blind Regression Modeling for Low Quality Data with Participatory Sensing

Participatory regression modeling is a cost-efficient mechanism to establish the relationships among multiple dimensions of sensory data collected from volunteers. Getting an accurate model estimate is challenging for two main reasons. First, with the concern of confidentiality of individual private data, the original data are nearly unavailable; second, low quality data with outliers are inherently embedded in the collected data. In this paper, we propose an innovative scheme, PURE, which can accurately estimate the global regression model without the need for knowing local private data (referred to as blind regression modeling ) even when there is a large portion of outliers embedded. The wisdom of PURE is to let individual participants peer judge and further improve the global estimate via negotiations. Meanwhile, during the whole process, all information is exchanged in an aggregated way. By design, PURE is secure and can well protect individual privacy. Furthermore, PURE is a lightweight protocol suitable for mobile devices. Extensive trace-driven simulation results show that PURE can achieve an outstanding accuracy gain of two orders of magnitude even with random outliers near a ratio of 50 percent compared with the state-of-the-art least square estimator.

Cross-calibration of participatory sensor networks for environmental noise mapping

Abstract Participatory measurements appear as a promising technique for performing noise mapping and monitoring. However, the confidence in the quality of raw data collected through participatory measurements controls the faithfulness of the output noise maps. In this paper, a cross-calibration method is proposed, which aims at both selecting the best candidate sensors and improving the furnished raw data. The method rests upon four steps: (i) an outlier detection, (ii) the crowd sensors-based correction, (iii) a fixed sensors-based correction, and (iv) the L den estimation. The efficiency of the approach for different characteristics of the network of mobile sensors is evaluated on its ability to reconstruct an artificial reference sound field, which consists of the one-month L 10s evolution, on a twenty streets network. The main conclusions are: (i) the systematic errors of the sensors can be efficiently corrected by a cross-calibration method, and thus do not affect the L den estimation, (ii) the fixed sensor network helps estimating the average error of the network of mobile sensors, (iii) the dispersion in an individual sensor measurements, which is due for example to the operator, stands for a much more critical concern and should be flagged by a rigorous outlier detection method, as the one proposed in this paper, (iv) although individual measures are improved by the proposed cross-calibration, some errors remain on the L den estimation because of the shortness of the collected samples, (v) increasing the number of sensors does not improve the L den estimation as long as individual measurements dispersions remain too large.

An Algorithm to Coordinate Measurements Using Stochastic Human Mobility Patterns in Large-Scale Participatory Sensing Settings

Participatory sensing is a promising new low-cost approach for collecting environmental data. However, current large-scale environmental participatory sensing campaigns typically do not coordinate the measurements of participants, which can lead to gaps or redundancy in the collected data. While some work has considered this problem, it has made several unrealistic assumptions. In particular, it assumes that complete and accurate knowledge about the participants future movements is available and it does not consider constraints on the number of measurements a user is willing to take. To address these shortcomings, we develop a computationally-efficient coordination algorithm (Best-match) to suggest to users where and when to take measurements. Our algorithm exploits human mobility patterns, but explicitly considers the inherent uncertainty of these patterns. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the state-of-the-art greedy and pull-based proximity algorithms in dynamic environments.

User Participatory Sensing for Disaster Detection and Mitigation

Rapid growth in communication bandwidth has enabled novel uses of mobile wireless technologies in areas such as smartphone-based user participatory sensing for disaster detection and mitigation. In this manuscript, we discuss novel approaches to resolve fundamental problems that currently hamper the effective utilization of user participatory sensing in this critical application domain. Our approaches to address major challenges related to energy efficiency, collaboration, privacy, ease of deployment, and robustness of communication can be integrated with external systems in a complementary manner to overcome the limitations of current disaster detection and mitigation systems that rely on expensive stationary devices.

Stochastic Optimal Control for Participatory Sensing Systems with Heterogenous Requests

We consider the crucial problem of maximizing the system-wide performance which takes into account request processing throughput, smartphone user experience and system stability in a participatory sensing system with cooperative smartphones. Three important controls need to be made, i.e., 1) request admission control, 2) task allocation, and 3) task scheduling on smartphones. It is highly challenging to achieve the optimal system-wide performance, given arbitrary unknown arrivals of sensing requests, intrinsic tradeoff between request processing throughput and smartphone user experience degradation, and heterogenous requests. Little existing work has studied this crucial problem of maximizing the system-wide performance of a participatory sensing system as a whole. In response to the challenges, we propose an optimal online control approach to maximize the system-wide performance of a participatory sensing system. Exploiting the stochastic Lyapunov optimization techniques, it derives the optimal online control strategies for request admission control, task allocation and task scheduling on smartphones. The most salient feature of our approach is that the achieved system-wide performance is arbitrarily close to the optimum, despite unpredictable and arbitrary request arrivals. Rigorous theoretical analysis and comprehensive simulation evaluation jointly demonstrate the efficacy of our online control approach.

Reputation-Based Incentives for Data Dissemination in Mobile Participatory Sensing Networks

The ever-more-capable smart mobile phone gave birth to a novel sensing paradigm, participatory sensing. In the application environment of mobile participatory sensing networks, mobile equipment is usually weakly connected. Due to uncertainty of connection, mobile nodes sometimes need encounter opportunities to accomplish data communication and transmission. However, the participants’ reluctance would diminish their enthusiasm if there is no incentive mechanism. To address this nontrivial issue, we propose reputation-based incentive schemes to motivate participants to disseminate reliable data in participatory sensing system, named RIDD, while minimizing incentive cost for maintaining sufficient number of reliable participants. When an intended receiver receives the data packet from a participant, the receiver authorizes the participant by an acknowledgment message within an encryption code automatically generated by the data packet, which serves as a proof of successful data delivery. RIDD evaluates participants using reputation degree calculated according to the encryption code, encouraging reliable participants to keep being interested in the participatory service with rewards. We conduct simulations in different scenarios. The results show that RIDD remarkably increases the winning probability of participants who disseminate accurate data and reduces the cost for retaining sufficient number of reliable participants.

Launching an Efficient Participatory Sensing Campaign

Participatory sensing is a promising sensing paradigm that enables collection, processing, dissemination and analysis of the phenomena of interest by ordinary citizens through their handheld sensing devices. Participatory sensing has huge potential in many applications, such as smart transportation and air quality monitoring. However, participants may submit low-quality, misleading, inaccurate, or even malicious data if a participatory sensing campaign is not launched effectively. Therefore, it has become a significant issue to establish an efficient participatory sensing campaign for improving the data quality. This article proposes a novel five-tier framework of participatory sensing and addresses several technical challenges in this proposed framework including: (1) optimized deployment of data collection points (DC-points); and (2) efficient recruitment strategy of participants. Toward this end, the deployment of DC-points is formulated as an optimization problem with maximum utilization of sensor and then a Wise-Dynamic DC-points Deployment (WD3) algorithm is designed for high-quality sensing. Furthermore, to guarantee the reliable sensing data collection and communication, a trajectory-based strategy for participant recruitment is proposed to enable campaign organizers to identify well-suited participants for data sensing based on a joint consideration of temporal availability, trust, and energy. Extensive experiments and performance analysis of the proposed framework and associated algorithms are conducted. The results demonstrate that the proposed algorithm can achieve a good sensing coverage with a smaller number of DC-points, and the participants that are termed as social sensors are easily selected, to evaluate the feasibility and extensibility of the proposed recruitment strategies.

Anonymity-Based Privacy-Preserving Data Reporting for Participatory Sensing

In this paper, we propose an efficient anonymous data reporting protocol for participatory sensing, which provide strong privacy protection, data accuracy, and generality. The protocol consists of two stages: 1) slot reservation and 2) message submission. In the slot reservation stage, a group of $N$ participants cooperate to assign each member a message slot in a vector which is essentially a message submission schedule, in such a manner that each participant’s slot is oblivious to other members and the application server. In the message submission stage, each participant transmits an encoded data to the application server based on the slot information known only to herself, in such a way that the application server cannot link a data to a specific participant. With such a data reporting protocol, the link between the data and the participants is broken, and as a result, participant’s privacy is protected. We conduct theoretical analysis of the correctness and anonymity of our protocol, as well as experiments to demonstrate the efficiency in small-scale applications with periodic data sampling.

A Self-Adaptive Behavior-Aware Recruitment Scheme for Participatory Sensing.

Participatory sensing services utilizing the abundant social participants with sensor-enabled handheld smart device resources are gaining high interest nowadays. One of the challenges faced is the recruitment of participants by fully utilizing their daily activity behavior with self-adaptiveness toward the realistic application scenarios. In the paper, we propose a self-adaptive behavior-aware recruitment scheme for participatory sensing. People are assumed to join the sensing tasks along with their daily activity without pre-defined ground truth or any instructions. The scheme is proposed to model the tempo-spatial behavior and data quality rating to select participants for participatory sensing campaign. Based on this, the recruitment is formulated as a linear programming problem by considering tempo-spatial coverage, data quality, and budget. The scheme enables one to check and adjust the recruitment strategy adaptively according to application scenarios. The evaluations show that our scheme provides efficient sensing performance as stability, low-cost, tempo-spatial correlation and self-adaptiveness.

Smart crowds in smart cities: real life, city scale deployments of a smartphone based participatory crowd management platform

We describe a platform for smart, city-wide crowd management based on participatory mobile phone sensing and location/situation specific information delivery. The platform supports quick and flexible deployments of end-to-end applications for specific events or spaces that include four key functionalities: (1) Mobile phone based delivery of event/space specific information to the users, (2) participatory sensor data collection (from app users) and flexible analysis, (3) location and situation specific message multicast instructing people in different areas to act differently in case of an emergency and (4) post mortem event analysis. This paper describes the requirements that were derived through a series of test deployments, the system architecture, the implementation and the experiences made during real life, large scale deployments. Thus, until today it has been deployed at 14 events in three European countries (UK, Netherlands, Switzerland) and was used by well over 100,000 people.

INSTAGRAMING IN RUSSIA

Instagram’s 200 million subscribers place it among the most popular Participatory Sensing Systems (PSSs) on the Internet today (de Melo et al. 2013). With its 62 million Internet users (Comscore, 2013), Russia is quickly becoming a powerful online market. With five million active daily users, Russia follows Brazil and US and comes ahead of UK by number of users (Alexa.com 2014).Being an early adopter of many technologies, the fashion industry actively uses image-based platform, by posting shots from runway shows, outfits of the day, pre-viewing new lines. Instagram is a no language barrier tool that is proving suitable for the visualization in the fashion industry.By observing the biggest Russian retailers TrendsBrands Instagram activity over a year, this paper outlines the most efficient way of brand communicating to consumers through images only as opposed to other mixed channel social media platforms employed, e.g. Facebook or Twitter. It studies the use of the most popular hashtags, influence of the photo sharing frequency as well as specific cultural behavior, which illustrates the promising potential of PSSs for the study of Russian online fashion environment. It also analyses the visual content that creates the biggest buzz. This research is complimented by in-depth interviews with a consumer sample from Trends-Brands follower to access the consumption interaction from the individual’s perspective.Deriving from Olbrich and Holsing (2011) business model of social shopping communities, Instagram is taken as an example. Discussing the phenomena of saling through Instagram, makes this platform a perfect example of social shopping community, which is are a mix of both: online shopping platform and social networking. Users of Instagram can share, comment, tag as well as purchase products online. Social shopping is a development of an online community that plays the role of both connecting consumers by communication and shopping (Olbrich and Holsing, 2011).Building up also on the Jenkins spredability (2013) theory as well Kozinetz (2010) nethnographic approach this research paper draws upon the distinctive features of Instagram as the channel not only to engage in the consumer-brand communication but also to communicate the consumption experience. This paper sets managerial implications for marketing communication managers who wish to conduct a successful strategy in Russia on Instagram.

Lab-on-Phone: a participatory sensing system

This paper presents a novel approach defined as laboratory on phone "Lab-on-Phone" which include the main features of "Human Centric Sensing", "Participatory Sensing" and "Ubiquitous Computing" paradigms in a multiuser and multipurpose acquisition, processing, storage and analysis chain infrastructure. Lab-on-Phone includes a generic sensing module which shares data with a Smartphone via near field communication. The sensor measurements are processed and displayed by an Android application running in the same Smartphone, a web server stores the measured information sent by multiple Smartphones, considering privacy and security. Lab-on-Phone also uses a set of web applications which allow accessing data from many different places and in any platform with Internet access.

Privacy and Quality Preserving Multimedia Data Aggregation for Participatory Sensing Systems

With the popularity of mobile wireless devices equipped with various kinds of sensing abilities, a new service paradigm named participatory sensing has emerged to provide users with brand new life experience. However, the wide application of participatory sensing has its own challenges, among which privacy and multimedia data quality preservations are two critical problems. Unfortunately, none of the existing work has fully solved the problem of privacy and quality preserving participatory sensing with multimedia data. In this paper, we propose SLICER, which is the first k-anonymous privacy preserving scheme for participatory sensing with multimedia data. SLICER integrates a data coding technique and message transfer strategies, to achieve strong protection of participants' privacy, while maintaining high data quality. Specifically, we study two kinds of data transfer strategies, namely transfer on meet up (TMU) and minimal cost transfer (MCT). For MCT, we propose two different but complimentary algorithms, including an approximation algorithm and a heuristic algorithm, subject to different strengths of the requirement. Furthermore, we have implemented SLICER and evaluated its performance using publicly released taxi traces. Our evaluation results show that SLICER achieves high data quality, with low computation and communication overhead.

Parameter Estimation Method for Time-Variant Target Object Using Randomly Deployed Sensors and Its Application to Participatory Sensing

We propose a method for estimating the size, perimeter length, and location of a time-variant target object moving in a monitored area. This method uses only binary information from sensors of which locations are unknown, where the binary information includes whether each sensor has detected the target object or not. Analysis based on integral geometry provides the relationship between the number of sensors detecting the target object and the target object parameters to be estimated. Because this relationship is linear, a linear filter, such as the Kalman filter, is applicable to estimate parameters if we can assume that the dynamics of the parameters are linear. As a concrete example, the size, shape, and location of an active thunder area is estimated. The model discussed in this paper is applicable as a model of participatory sensing.

An Event-Driven QoI-Aware Participatory Sensing Framework with Energy and Budget Constraints

Participatory sensing systems can be used for concurrent event monitoring applications, like noise levels, fire, and pollutant concentrations. However, they are facing new challenges as to how to accurately detect the exact boundaries of these events, and further, to select the most appropriate participants to collect the sensing data. On the one hand, participants’ handheld smart devices are constrained with different energy conditions and sensing capabilities, and they move around with uncontrollable mobility patterns in their daily life. On the other hand, these sensing tasks are within time-varying quality-of-information (QoI) requirements and budget to afford the users’ incentive expectations. Toward this end, this article proposes an event-driven QoI-aware participatory sensing framework with energy and budget constraints. The main method of this framework is event boundary detection. For the former, a two-step heuristic solution is proposed where the coarse-grained detection step finds its approximation and the fine-grained detection step identifies the exact location. Participants are selected by explicitly considering their mobility pattern, required QoI of multiple tasks, and users’ incentive requirements, under the constraint of an aggregated task budget. Extensive experimental results, based on a real trace in Beijing, show the effectiveness and robustness of our approach, while comparing with existing schemes.

An Introduction to the Special Issue on Participatory Sensing and Crowd Intelligence

editorial Free AccessAn Introduction to the Special Issue on Participatory Sensing and Crowd Intelligence Authors: Bin Guo Northwestern Polytechnical University, China Northwestern Polytechnical University, ChinaView Profile , Alvin Chin Nokia, Beijing, China Nokia, Beijing, ChinaView Profile , Zhiwen Yu Northwestern Polytechnical University, China Northwestern Polytechnical University, ChinaView Profile , Runhe Huang Hosei University, Tokyo, Japan Hosei University, Tokyo, JapanView Profile , Daqing Zhang Institut TELECOM SudParis, France Institut TELECOM SudParis, FranceView Profile Authors Info & Claims ACM Transactions on Intelligent Systems and TechnologyVolume 6Issue 3May 2015 Article No.: 36pp 1–4https://doi.org/10.1145/2745712Published:21 April 2015Publication History 5citation311DownloadsMetricsTotal Citations5Total Downloads311Last 12 Months18Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Ohmage

Participatory sensing (PS) is a distributed data collection and analysis approach where individuals, acting alone or in groups, use their personal mobile devices to systematically explore interesting aspects of their lives and communities [Burke et al. 2006]. These mobile devices can be used to capture diverse spatiotemporal data through both intermittent self-report and continuous recording from on-board sensors and applications. Ohmage (http://ohmage.org) is a modular and extensible open-source, mobile to Web PS platform that records, stores, analyzes, and visualizes data from both prompted self-report and continuous data streams. These data streams are authorable and can dynamically be deployed in diverse settings. Feedback from hundreds of behavioral and technology researchers, focus group participants, and end users has been integrated into ohmage through an iterative participatory design process. Ohmage has been used as an enabling platform in more than 20 independent projects in many disciplines. We summarize the PS requirements, challenges and key design objectives learned through our design process, and ohmage system architecture to achieve those objectives. The flexibility, modularity, and extensibility of ohmage in supporting diverse deployment settings are presented through three distinct case studies in education, health, and clinical research.

Quality of Contributed Service and Market Equilibrium for Participatory Sensing

User-contributed or crowd-sourced information is becoming increasingly common. In this paper, we consider the specific case of participatory sensing whereby people contribute information captured by sensors, typically those on a smartphone, and share the information with others. We propose a new metric called quality of contributed service (QCS) which characterizes the information quality and timeliness of a specific real-time sensed quantity achieved in a participatory manner. Participatory sensing has the problem that contributions are sporadic and infrequent. To overcome this, we formulate a market-based framework for participatory sensing with plausible models of the market participants comprising data contributors, service consumers and a service provider. We analyze the market equilibrium and obtain a closed form expression for the resulting QCS at market equilibrium. Next, we examine the effects of realistic behaviors of the market participants and the nature of the market equilibrium that emerges through extensive simulations. Our results show that, starting from purely random behavior, the market and its participants can converge to the market equilibrium with good QCS within a short period of time.