Participatory Sensing Research Articles

Privacy Preserving Utility-Aware Mechanism for Data Uploading Phase in Participatory Sensing

Participatory-sensing systems leverage mobile phones to offer unprecedented services that improve users’ quality of life. However, the data collection process may compromise participants’ privacy when reporting measurements tagged or correlated with their sensitive information. Therefore, existing privacy-preserving techniques introduce data perturbation, which ensures privacy guarantees, yet at the cost of a loss of data utility, a major concern for queriers. Different from past works, we assess simultaneously the two competing goals of ensuring data quality for queriers and protecting participants’ privacy. We propose a general privacy-preserving mechanism to capture the privacy inference threat encountered by a participant while considering utility requirements set by data queriers. We rely on a general probabilistic privacy mechanism, which is run on a trust-worthy entity to distort the collected data before its release. We consider two different adversary models and propose appropriate solutions for the both of them. Furthermore, we tackle the challenge of participatory collected data with large size alphabets by investigating quantization techniques. The proposed PRivacy-preserving Utility-aware Mechanism, PRUM, was evaluated on three different real datasets while varying the distribution of the collected data and the obfuscation type. The obtained results demonstrate that, for different applications, a limited distortion may ensure the participants’ privacy while maintaining about 98 percent of the required data utility.

Predictive Location Aware Online Admission and Selection Control in Participatory Sensing

Participatory sensing is a crowdsourcing-based framework, where the platform executes the sensing requests with the help of many common peoples’ handheld devices (typically smartphones). In this paper, we mainly address the online sensing request admission and smartphone selection problem to maximize the profit of the platform, taking into account the queue backlog, and the location of sensing requests and smartphones. First, we formulate this problem as a discrete time model and design a location aware online admission and selection control algorithm (LAAS) based on the Lyapunov optimization technique. The LAAS algorithm only depends on the currently available information and makes all the control decisions independently and simultaneously. Next, we utilize the recent advancement of the accurate prediction of smartphones’ mobility and sensing request arrival information in the next few time slots and develop a predictive location aware admission and selection control algorithm (PLAAS). We further design a greedy predictive location aware admission and selection control algorithm (GPLAAS) to achieve the online implementation of PLAAS approximately and iteratively. Theoretical analysis shows that under any control parameter V > 0, both LAAS and PLAAS algorithm can achieve O (1/ V )-optimal average profit, while the sensing request backlog is bounded by O ( V ). Extensive numerical results based on both synthetic and real trace show that LAAS outperforms the Greedy algorithm and Random algorithm and GPLAAS improves the profit-backlog tradeoff over LAAS.

Efficient User Guidance for Validating Participatory Sensing Data

Participatory sensing has become a new data collection paradigm that leverages the wisdom of the crowd for big data applications without spending cost to buy dedicated sensors. It collects data from human sensors by using their own devices such as cell phone accelerometers, cameras, and GPS devices. This benefit comes with a drawback: human sensors are arbitrary and inherently uncertain due to the lack of quality guarantee. Moreover, participatory sensing data are time series that exhibit not only highly irregular dependencies on time but also high variance between sensors. To overcome these limitations, we formulate the problem of validating uncertain time series collected by participatory sensors. In this article, we approach the problem by an iterative validation process on top of a probabilistic time series model. First, we generate a series of probability distributions from raw data by tailoring a state-of-the-art dynamical model, namely <u>G</u>eneralised <u>A</u>uto <u>R</u>egressive <u>C</u>onditional <u>H</u>eteroskedasticity (GARCH), for our joint time series setting. Second, we design a feedback process that consists of an adaptive aggregation model to unify the joint probabilistic time series and an efficient user guidance model to validate aggregated data with minimal effort. Through extensive experimentation, we demonstrate the efficiency and effectiveness of our approach on both real data and synthetic data. Highlights from our experiences include the fast running time of a probabilistic model, the robustness of an aggregation model to outliers, and the significant effort saving of a guidance model.

Structural Reliability Estimation with Participatory Sensing and Mobile Cyber-Physical Structural Health Monitoring Systems

With the help of community participants, smartphones can become useful wireless sensor network (WSN) components, form a self-governing structural health monitoring (SHM) system, and merge structural mechanics with participatory sensing and server computing. This paper presents a methodology and framework of such a cyber-physical system (CPS) that generates a bridge finite element model (FEM) integrated with vibration measurements from smartphone WSNs and centralized/distributed computational facilities, then assesses structural reliability based on updated FEMs. Structural vibration data obtained from smartphones are processed on a server to identify modal frequencies of an existing bridge. Without design drawings and supportive documentation but field measurements and observations, FEM of the bridge is drafted with uncertainties in the structural mass, stiffness, and boundary conditions (BCs). Then, 2700 FEMs are autonomously generated, and the baseline FEM is updated by minimizing the error between the crowdsourcing-based modal identification results and the FEM analysis. Furthermore, using 151 strong ground motion records from databases, the bridge response time history simulations are conducted to obtain displacement demand distribution. Finally, based on reference performance criteria, structural reliability of the bridge is estimated. Integrating the cyber (FEM analysis) and the physical (the bridge structure and measured vibration characteristics) worlds, this crowdsourcing-based CPS can provide a powerful tool for supporting rapid, remote, autonomous, and objective infrastructure-related decision-making. This study presents a new example of the emerging fourth industrial revolution from structural engineering and SHM perspective.

Mobile Tools for Community Scientists

Mobile Tools for Community Scientists

PS-Sim: A framework for scalable data simulation and incentivization in participatory sensing-based smart city applications

The widespread penetration of smartphone has paved the way for a new paradigm of pervasive computing, known as the participatory sensing (PS). In PS, a human user explicitly performs the tasks of sensing and reporting, typically in lieu of incentives. One major limitation with PS is the sparsity of data owing to the lack of active participation, thus inhibiting large scale real-life experiments for the research community. In our preliminary work (Barnwal et al., 2018), we propose a spatio-temporal event occurrence and report generation based data simulation framework called PS-Sim. This paper extends the PS-Sim framework with a novel budget allocation mechanism for incentivizing participants. The allocation mechanism guarantees the presence of a threshold number of active participants and also ensures the reporting of the significant fraction of events for sustainability of PS applications. The simulation environment provided by the PS-Sim framework replicates real participation and event occurrence behaviors, which is expected to enable the domain experts to investigate and assess the requisites (benefits and challenges) of introducing smart city applications. As a part of the evaluation of the budget allocation mechanism, we study its performance under varying effects of reward and participation, and establish its fairness as far as the remuneration of active participants is concerned.

Trust-Based IoT Cloud Participatory Sensing of Air Quality

In this paper we present a case study of IoT cloud participatory sensing where a user sends a query to the cloud about the air quality of a location at a particular time to decide if it should enter the location based on its susceptibility to the air quality detected. All IoT devices (e.g., smart phones carried by humans or smart cars driven by humans) capable of detecting air quality can act as participants and submit sensing reports to the cloud for sensing result aggregation. The major challenge is the selection of trustworthy participants because not all IoT devices will be trustworthy. We leverage a “Trust as a Service” (TaaS) cloud utility to address the issue of selecting trustworthy participants. Using real traces of ozone (O3) levels and mobility traces of users in the O3 community of interest (O3COI) group in the city of Houston, we demonstrate that TaaS outperforms contemporary IoT trust protocols in selecting trustworthy participants. We compare the performance of the TaaS cloud utility with two contemporary IoT trust protocols for supporting trust-based IoT participatory sensing applications. With the help of the TaaS cloud utility, a user in this O3COI group is able to obtain O3 readings very close to the ground truth O3 level despite 30% participants are untrustworthy.

Missing observation approximation for spatio-temporal profile reconstruction in participatory sensor networks

Purpose Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental phenomenon – P monitoring applications dealing with noise pollution, road traffic, requiring spatio-temporal data samples of P (to capture its variations and its profile construction) in the region of interest – can be enabled using PWSN. Because of irregular distribution and uncontrollable mobility of people (with mobile phones), and their willingness to participate, complete spatio-temporal (CST) coverage of P may not be ensured. Therefore, unobserved data values must be estimated for CST profile construction of P and presented in this paper. Design/methodology/approach In this paper, the estimation of these missing data samples both in spatial and temporal dimension is being discussed, and the paper shows that non-parametric technique – Kernel Regression – provides better estimation compared to parametric regression techniques in PWSN context for spatial estimation. Furthermore, the preliminary results for estimation in temporal dimension have been provided. The deterministic and stochastic approaches toward estimation in the context of PWSN have also been discussed. Findings For the task of spatial profile reconstruction, it is shown that non-parametric estimation technique (kernel regression) gives a better estimation of the unobserved data points. In case of temporal estimation, few preliminary techniques have been studied and have shown that further investigations are required to find out best estimation technique(s) which may approximate the missing observations (temporally) with considerably less error. Originality/value This study addresses the environmental informatics issues related to deterministic and stochastic approaches using PWSN.

Analyzing Spatial and Temporal User Behavior in Participatory Sensing

The large number of mobile devices and their increasingly powerful computing and sensing capabilities have enabled the participatory sensing concept. Participatory sensing applications are now able to effectively collect a variety of information types with high accuracy. Success, nevertheless, depends largely on the active participation of the users. In this article, we seek to understand spatial and temporal user behaviors in participatory sensing. To do so, we conduct a large-scale deployment of Citizense, a multi-purpose participatory sensing framework, in which 359 participants of demographically different backgrounds were simultaneously exposed to 44 participatory sensing campaigns of various types and contents. This deployment has successfully gathered various types of urban information and at the same time portrayed the participants’ different spatial, temporal and behavioral patterns. From this deployment, we can conclude that (i) the Citizense framework can effectively help participants to design data collecting processes and collect the required data, (ii) data collectors primarily contribute in their free time during the working week; much fewer submissions are done during the weekend, (iii) the decision to respond and complete a particular participatory sensing campaign seems to be correlated to the campaign’s geographical context and/or the recency of the data collectors’ activities, and (iv) data collectors can be divided into two groups according to their behaviors: a smaller group of active data collectors who frequently perform participatory sensing activities and a larger group of regular data collectors who exhibit more intermittent behaviors. These identified user behaviors open avenues to improve the design and operation of future participatory sensing applications.

QLDS: A Novel Design Scheme for Trajectory Privacy Protection with Utility Guarantee in Participatory Sensing

Participatory sensing, leveraging on the ubiquity of cheap sensors in mobile devices, enables various promising applications of great social benefit. However, its ubiquitous sampling and openness results in serious privacy concerns. People's activity trajectories may reveal their private information such as home and work place and thus requires proper protection. In this paper, we propose a novel design scheme, called query logics detached storage (QLDS), for trajectory privacy protection. The core idea of QLDS is to extract the query logics for personal trajectory retrieval and make actual trajectory tuples not clustered to any route-identity or user-identity at server end, which introduces fine-granularity anonymity. The QLDS design scheme stores the extracted query logics at users own client devices and the un-clustered location tuples at the backend server, guaranteeing trajectory reconstruction at client-end and privacy preservation at server-end. Besides, integration of QLDS with other privacy protection approaches can further enhance the protection strength and bring flexible configurations to meet individuals’ variable privacy concerns. The theoretical analytics is provided for the privacy and utility evaluation. The data retrieval performance of QLDS is experimentally evaluated in real-world internet environment.

Distributed Social Welfare Maximization in Urban Vehicular Participatory Sensing Systems

We consider the crucial problem of maximizing the social welfare of a vehicular participatory sensing system, where the system's social welfare is measured by the amount of sensing data delivered to a central platform through a vehicular ad hoc network. The key to the problem is to control network stability since both network congestion and idleness will slump system social welfare. However, several great challenges exist. First, limited vehicle-to-vehicle (V2V) link capacity and vehicle buffer size will lead to heavy network congestion when each individual vehicle blindly injects too much data into the network hoping to get more rewards. Second, the highly dynamic network topology and stochastic inter-vehicle contacts have a serious impact on the performance of multi-hop data transmission. Third, vehicles need to be practically rewarded based on their sensing and transmission cost, which, however, greatly vary among vehicles. To tackle the aforementioned challenges, we propose a distributed backpressure control approach, the first work to the best of our knowledge, to maximize the social welfare while balancing network stability for a vehicular participatory sensing system. Combining vehicular network properties and Lyapunov optimization techniques, individualized strategies are developed for each participant to control its sensing rate, make its own routing decisions, and set its own price for data relaying. Formally proved by rigorous theoretical analysis, the social welfare achieved by the proposed approach is comparative to the optimum performance. In addition, extensive data-driven simulations based on real taxi GPS traces have been conducted, and the results confirm the efficacy of the proposed algorithm.

Privacy-preserving collaborative fuzzy clustering

The proliferation of Internet of Things devices has contributed to the emergence of participatory sensing (PS), where multiple individuals collect and report their data to a third-party data mining cloud service for analysis. The need for the participants to collaborate with each other for this analysis gives rise to the concept of collaborative learning. However, the possibility of the cloud service being semi-honest poses a key challenge: preserving the participants' privacy.In this paper, we address this challenge with a two-stage scheme called RG+RP: in the first stage, each participant perturbs his/her data by passing the data through a nonlinear function called repeated Gompertz (RG); in the second stage, he/she then projects his/her perturbed data to a lower dimension in an (almost) distance-preserving manner, using a specific random projection (RP) matrix. The nonlinear RG function is designed to mitigate maximum a posteriori (MAP) estimation attacks, while random projection resists independent component analysis (ICA) attacks and ensures clustering accuracy. The proposed two-stage randomisation scheme is assessed in terms of its recovery resistance to MAP estimation attacks. Preliminary theoretical analysis as well as experimental results on synthetic and real-world datasets indicate that RG+RP has better recovery resistance to MAP estimation attacks than most state-of-the-art techniques. For clustering, fuzzy c-means (FCM) is used. Results using seven cluster validity indices, root mean squared error (RMSE) and accuracy ratio show that clustering results based on two-stage-perturbed data are comparable to the clustering results based on raw data — this confirms the utility of our privacy-preserving scheme when used with either FCM or HCM.

Do Monetary Incentives Influence Users' Behavior in Participatory Sensing?

Participatory sensing combines the powerful sensing capabilities of current mobile devices with the mobility and intelligence of human beings, and as such has to potential to collect various types of information at a high spatial and temporal resolution. Success, however, entirely relies on the willingness and motivation of the users to carry out sensing tasks, and thus it is essential to incentivize the users’ active participation. In this article, we first present an open, generic participatory sensing framework (Citizense) which aims to make participatory sensing more accessible, flexible and transparent. Within the context of this framework we adopt three monetary incentive mechanisms which prioritize the fairness for the users while maintaining their simplicity and portability: fixed micro-payment, variable micro-payment and lottery. This incentive-enabled framework is then deployed on a large scale, real-world case study, where 230 participants were exposed to 44 different sensing campaigns. By randomly distributing incentive mechanisms among participants and a subset of campaigns, we study the behaviors of the overall population as well as the behaviors of different subgroups divided by demographic information with respect to the various incentive mechanisms. As a result of our study, we can conclude that (1) in general, monetary incentives work to improve participation rate; (2) for the overall population, a general descending order in terms of effectiveness of the incentive mechanisms can be established: fixed micro-payment first, then lottery-style payout and finally variable micro-payment. These two conclusions hold for all the demographic subgroups, even though different different internal distances between the incentive mechanisms are observed for different subgroups. Finally, a negative correlation between age and participation rate was found: older participants contribute less compared to their younger peers.

A Geoprivacy by Design Guideline for Research Campaigns That Use Participatory Sensing Data.

Participatory sensing applications collect personal data of monitored subjects along with their spatial or spatiotemporal stamps. The attributes of a monitored subject can be private, sensitive, or confidential information. Also, the spatial or spatiotemporal attributes are prone to inferential disclosure of private information. Although there is extensive problem-oriented literature on geoinformation disclosure, our work provides a clear guideline with practical relevance, containing the steps that a research campaign should follow to preserve the participants’ privacy. We first examine the technical aspects of geoprivacy in the context of participatory sensing data. Then, we propose privacy-preserving steps in four categories, namely, ensuring secure and safe settings, actions prior to the start of a research survey, processing and analysis of collected data, and safe disclosure of datasets and research deliverables.

Incentive Mechanism in Participatory Sensing for Ambient Assisted Living

Incentive Mechanism in Participatory Sensing for Ambient Assisted Living

A Privacy-Preserving Incentive Mechanism for Participatory Sensing Systems

The proliferation of mobile devices has facilitated the prevalence of participatory sensing applications in which participants collect and share information in their environments. The design of a participatory sensing application confronts two challenges: “privacy” and “incentive” which are two conflicting objectives and deserve deeper attention. Inspired by physical currency circulation system, this paper introduces the notion of E-cent, an exchangeable unit bearer currency. Participants can use the E-cent to take part in tasks anonymously. By employing E-cent, we propose an E-cent-based privacy-preserving incentive mechanism, called EPPI. As a dynamic balance regulatory mechanism, EPPI can not only protect the privacy of participant, but also adjust the whole system to the ideal situation, under which the rated tasks can be finished at minimal cost. To the best of our knowledge, EPPI is the first attempt to build an incentive mechanism while maintaining the desired privacy in participatory sensing systems. Extensive simulation and analysis results show that EPPI can achieve high anonymity level and remarkable incentive effects.

Adaptive Reward Allocation for Participatory Sensing

Participatory sensing is a paradigm through which mobile device users (or participants) collect and share data about their environments. The data captured by participants is typically submitted to an intermediary (the service provider) who will build a service based upon this data. For a participatory sensing system to attract the data submissions it requires, its users often need to be incentivized. However, as an environment is constantly changing (for example, an accident causing a buildup of traffic and elevated pollution levels), the value of a given data item to the service provider is likely to change significantly over time, and therefore an incentivization scheme must be able to adapt the rewards it offers in real‐time to match the environmental conditions and current participation rates, thereby optimizing the consumption of the service provider’s budget. This paper presents adaptive reward allocation (ARA), which uses the Lyapunov Optimization method to provide adaptive reward allocation that optimizes the consumption of the service provider’s budget. ARA is evaluated using a simulated participatory sensing environment with experimental results showing that the rewards offered to participants are adjusted so as to ensure that the data captured matches the dynamic changes occurring in the sensing environment and takes the response rate into account while also seeking to optimize budget consumption.

MPiLoc: Self-Calibrating Multi-Floor Indoor Localization Exploiting Participatory Sensing

While location is one of the most important context information in mobile and pervasive computing, large-scale deployment of indoor localization system remains elusive. In this work, we propose MPiLoc, a multi-floor indoor localization system that utilizes data contributed by smartphone users through participatory sensing for automatic floor plan and radio map construction. Our system does not require manual calibration, prior knowledge, or infrastructure support. The key novelty of MPiLoc is that it clusters and merges walking trajectories annotated with sensor and signal strengths to derive a map of walking paths annotated with radio signal strengths in multi-floor indoor environments. We evaluate MPiLoc over five different indoor areas. Evaluation shows that our system can derive indoor maps for various indoor environments in multi-floor settings and achieve an average localization error of 1.82 m.

Energy-Efficient Event Detection by Participatory Sensing Under Budget Constraints

Dynamic event detection by using participatory sensing paradigms has received growing interests recently, where detection tasks are assigned to smart-device users who can potentially collect needed sensory data from device-equipped sensors. Typical applications include, but are not limited to, noise and air pollution detections, people gathering, even disaster prediction. Given this problem, although many existing centralized solutions are effective and widely used, they usually cause heavy communication overhead. Thus, it is strongly desired to design distributed solutions to reduce energy consumption, while achieving a high level of detection accuracy with limited sensing task budget. In this paper, we first present two novel centralized detection algorithms as the performance benchmark, which make use of the Minimum Cut theory and support vector machine (SVM)-based pattern recognition techniques. Then, we introduce a novel distributed and energy-efficient event detection framework under task budget constraint, where we formulate an optimization problem and derive an optimal utility function. Finally, based on a real trace-driven data set in an urban area of Beijing, extensive simulation results demonstrate the effectiveness of our proposed algorithms.

On the Comparison of the Social Dynamic of Large Cities through Participatory Sensing

The dynamics of cities have been studied over the years with various applications such as urban planning, disease propagation, traffic forecasting, local recommendations and studies of human social behavior. However, with the technological evolution, especially the popularization of smartphones and the Internet, a new opportunity is presented to carry out such studies: the use of social media data for the population study. In this context, this paper aims to present a new way to compare cities, using as similarity measure of the pattern of social mobility of its inhabitants. To validate the study, 1,601,323 Foursquare check-ins were used spread over 10 cities in a period of 33 days.