Participatory Sensing Systems Research Articles

Mobile Participatory Sensing Systems: A Comprehensive Review

Mobile Participatory Sensing Systems have the potential to improve different services through monitoring of the urban landscape using mobile devices based on the collaboration of thousands of mobile users. Many articles have been recently published related to mobile participatory syst

Venue-Popularity Prediction Using Social Data Participatory Sensing Systems and RNNs

Analyzing social data as a participatory sensing system (PSS) provides a deep understanding of city dynamics, such as people's mobility patterns, social patterns, and events detection. In a PSS, individuals with mobile devices sense their environment, collect, and share data. For smart cities, intelligent city dynamics analysis has many applications such as for urban planning, transportation systems, city environment, energy consumption, public safety, and city economy. This study aimed to develop an intelligent application to predict the potential number of visitors for specific venues based on the analysis of mobility patterns of individuals. The ability to accurately predict the number of visitors to a venue allows authorities to better understand the behavior of the people and allocate recourses accordingly. We formulated the venue-popularity problem as a sequence-based regression and classification problem. We employed three recurrent neural network (RNN)-based models to predict the locations of popular venues on a city map. The proposed models include basic RNN, long short-term memory (LSTM), and gated recurrent unit (GRU). We constructed several social datasets for Riyadh city using Twitter and Foursquare as the PSS. Our results revealed that modeling venue-popularity prediction as a sequence regression problem yields better results than modeling it as a sequence classification problem. For the city-popularity map prediction problem, the vector autoregression baseline model achieved better performance than the RNN-family models.

Multi-Dimensional Anonymization for Participatory Sensing Systems

Participatory sensing systems are designed to enable community people to collect, analyze, and share information for their mutual benefit in a cost-effective way. The apparently insensitive information transmitted in plaintext through the inexpensive infrastructure can be used by an eavesdrop-per to infer some sensitive information and threaten the privacy of the partic-ipating users. Participation of users cannot be ensured without assuring the privacy of the participants. Existing techniques add some uncertainty to the actual observation to achieve anonymity which, however, diminishes data quality/utility to an unacceptable extent. The subset-coding based anonymiza-tion technique, DGAS [LCN 16] provides the desired level of privacy. In this research, our objective is to overcome this limitation and design a scheme with broader applicability. We have developed a computationally efficient sub-set-coding scheme and also present a multi-dimensional anonymization tech-nique that anonymizes multiple properties of user observation, e.g. both loca-tion and product association of an observer in the context of consumer price sharing application. To the best of our knowledge, it is the first work which supports multi-dimensional anonymization in PSS. This paper also presents an in-depth analysis of adversary threats considering collusion of adversaries and different report interception patterns. Theoretical analysis, comprehensive simulation, and Android prototype based experiments are carried out to estab-lish the applicability of the proposed scheme. Also, the adversary capability is simulated to prove our scheme’s effectiveness against privacy risk.

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.

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.

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.

Incentives-based preferences and mobility-aware task assignment in participatory sensing systems

Participatory Sensing (PS) systems rely essentially on users’ willingness to dedicate their devices’ resources (energy, processing time..) to contribute high-quality data about various phenomena. In this paper, we study the critical issue of participants’ recruitment in PS systems in the aim of minimizing the overall sensing time. First, we design the users’ arrival and acceptance/rejection models. Further, we introduce two variants of task assignment mechanisms; without and with incentives. In the former model, we enhance our proposed scheme, P-MATA, for preferences and mobility-aware task assignment, by introducing a logit regressing-based preferences model. Thus, we estimate the users’ acceptance probabilities as function of the number and loads of sensing tasks. We incorporate rewards as a third attribute in the second variant of assignment scheme and propose two different incentivizing policies to study their impact on enhancing users’ acceptance. Incentives are either task priority-based or data quality-based. All proposed algorithms adopt a greedy-based selection strategy and address the minimization of the average makespan of all sensing tasks. We conduct extensive performance evaluation based on real traces while varying the number of tasks and associated workloads. Results proved that incentivizing participants has intensified their commitment by achieving lower average makespan and higher number of delegated tasks. Moreover, quality-based incentivizing mechanism realized the better performance while minimizing the dedicated budget.

Distributed Localization for Participatory Sensing Systems

The focus of this paper is on participatory sensing in which every participant carrying smartphone senses its environment and shares it with server. Most of the applications require location information to perform sensing activity. But, GPS drains considerable amount of energy if used for localization. There exists a centralized localization approach to detect a set of devices for the role of broadcaster which must turn on GPS. Its neighbouring devices rely on them to calculate their position. In this paper, we provide a novel distributed solution for finding roles for the participating mobile devices. It is important in real scenario to alleviate server from the burden of assigning roles. Our proposed approach does not make any assumption about the presence of infrastructure or device capabilities, other than the availability of WiFi and the ability to know devices within the WiFi range. To the best of our knowledge, this is the first protocol for distributed localization in participatory sensing system. Simulation results using real dataset demonstrate that our algorithm is capable of assigning role in 70-85% less time compared to the centralized algorithm but consumes 12-15% more energy as it does not find the optimal set of broadcasters which requires global information.

A-PIE: An algorithm for preserving privacy, quality of information, and energy consumption in Participatory Sensing Systems

We present A-PIE, a hybrid privacy-preserving mechanism for Participatory Sensing Systems that provides a high level of privacy protection as well as a high quality of information while minimizing the energy consumption. A-PIE takes into consideration the variability of the variable of interest to identify clusters, and divides the target area in cells of different sizes. A-PIE applies anonymization or double-encryption to balance privacy protection, quality of information and energy consumption based on the cell’s size. Extensive experimentation, using a real air monitoring system, shows the superior performance of the proposed mechanism when compared with most important privacy-preserving mechanisms.

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.

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.

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.

Participatory Sensing System in Presence of Multiple Buyers

Abstract Participatory sensing system (PSS) is an emerging field of research where the fundamental idea is to employ the autonomous agents (in this paper humans) carrying smart devices, as a virtual sensor for collecting and transmitting information. The most grinding work is to motivate the human agents carrying smart devices in the data collecting process. For motivating the human agents, auction theory has played a central role with a framework to incentivize the agents in the PSS. In this line, works so far have mainly considered that there are several human agents to sell the data, and one data collecting centre to buy the data. In this paper, we propose a truthful mechanism in an online double auction environment that addresses the situation where multiple sellers and multiple buyers are present in the PSS. To the best of our knowledge, this is the first truthful mechanism in double auction setting.

A Location Prediction Algorithm with Daily Routines in Location-Based Participatory Sensing Systems

Mobile node location predication is critical to efficient data acquisition and message forwarding in participatory sensing systems. This paper proposes a social-relationship-based mobile node location prediction algorithm using daily routines (SMLPR). The SMLPR algorithm models application scenarios based on geographic locations and extracts social relationships of mobile nodes from nodes’ mobility. After considering the dynamism of users’ behavior resulting from their daily routines, the SMLPR algorithm preliminarily predicts node’s mobility based on the hidden Markov model in different daily periods of time and then amends the prediction results using location information of other nodes which have strong relationship with the node. Finally, the UCSD WTD dataset are exploited for simulations. Simulation results show that SMLPR acquires higher prediction accuracy than proposals based on the Markov model.

UrbanMobilitySense: A User-Centric Participatory Sensing System for Transportation Activity Surveys

Transportation activity surveys collect the travel behavior of people, including when, where, and how they travel for urban planning purposes. Traditionally, transportation activity surveys are carried out using conventional questionaires, which are labor intensive and error prone. In this paper, we have developed a smartphone-based mobility sensing system, called UrbanMobilitySense, which captures human mobility information automatically to conduct transportation activity surveys. The UrbanMobilitySense system was designed to address two critical issues: 1) energy conservation and 2) privacy preservation. To optimize the energy utilization of smartphone, we avoid using the GPS sensor when the user is at long-stay places and filter out redundant data before data uploading. To preserve personal privacy, each smartphone maintains the user's long-stay places by two separate profiles: 1) private place profile and 2) public place profile. The former maintains the privacy-preserved places (e.g., home), whereas the latter maintains the public places (e.g., parks). We implement the UrbanMobilitySense system to conduct real-world transportation activity surveys, study the performance of our system through extensive experiments, and analyze the computational complexity of the proposed algorithms. The outcome of our work has been deployed in Singapore to support the Land Transport Authority's transportation activity surveys.

Improving Data Quality with an Accumulated Reputation Model in Participatory Sensing Systems

The ubiquity of mobile devices brings forth a sensing paradigm, participatory sensing, to collect and interpret sensory information from the environment. Participants join in multifarious sensing tasks and share their data. The sensing result can be obtained in light of shared data. It is not uncommon that some corrupted data is provided by participants, which makes sensing result unreliable accordingly. To address this nontrivial issue, we proposed the accumulated reputation model (ARM) to improve the accuracy of the sensing result. In ARM, participants' reputation will be computed and accumulated based on their sensing data. The sensing data from reputable participants make higher contributions to the sensing result. ARM performs well on calculating accurate sensing results, even in extreme scenarios, where there are many inexperienced or malicious participants.

A Reputation Framework for Social Participatory Sensing Systems

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 ...

On Sensor Data Verification for Participatory Sensing Systems

In this paper we study the problem of sensor data verification in Participatory Sensing (PS) systems using an air quality/pollution monitoring application as a validation example. Data verification, in the context of PS, consists of the process of detecting and removing spatial outliers to properly reconstruct the variables of interest. We propose, implement, and test a hybrid neighborhood-aware algorithm for outlier detection that considers the uneven spatial density of the users, the number of malicious users, the level of conspiracy, and the lack of accuracy and malfunctioning sensors. The algorithm utilizes the Delaunay triangulation and Gaussian Mixture Models to build neighborhoods based on the spatial and non-spatial attributes of each location. This neighborhood definition allows us to demonstrate that it is not necessary to apply accurate but computationally expensive estimators to the entire dataset to obtain good results, as equally accurate but computationally cheaper methods can also be applied to part of the data and obtain good results as well. Our experimental results show that our hybrid algorithm performs as good as the best estimator while reducing the execution time considerably.

Data interpolation for participatory sensing systems

In this paper, we study the problem of applying data interpolation techniques in Participatory Sensing (PS) systems using an air quality/pollution monitoring application as an example. While traditional environmental monitoring systems consist of very few static measuring stations, PS systems rely on the participation of many mobile stations. As a result, the structure of the data provided by each system is different and instead of a multivariate time series with a few gaps in the same space, now we have a multivariate time-space series with many gaps in time and space. First, two data interpolation techniques, Markov Random Fields and kriging, are analyzed. After showing the trade-offs and superiority of kriging, this technique is used to perform a one-variable data interpolation. Then, the problems of cokriging for multivariate interpolation are introduced and Principal Component Analysis and Independent Component Analysis are utilized along with kriging to overcome these problems. Finally, an alternative approach to interpolate data in time and space is proposed, which is really useful for PS systems. The results indicate that the accuracy of the estimates improves with the amount of data, i.e., one variable, multiple variables, and space and time data. Also, the results clearly show the advantage of a PS system compared with a traditional measuring system in terms of the precision and granularity of the information provided to the users.

TAPAS: Trustworthy privacy-aware participatory sensing

With the advent of mobile technology, a new class of applications, called participatory sensing (PS), is emerging, with which the ubiquity of mobile devices is exploited to collect data at scale. However, privacy and trust are the two significant barriers to the success of any PS system. First, the participants may not want to associate themselves with the collected data. Second, the validity of the contributed data is not verified, since the intention of the participants is not always clear. In this paper, we formally define the problem of privacy and trust in PS systems and examine its challenges. We propose a trustworthy privacy-aware framework for PS systems dubbed TAPAS, which enables the participation of the users without compromising their privacy while improving the trustworthiness of the collected data. Our experimental evaluations verify the applicability of our proposed approaches and demonstrate their efficiency.