Quality-Aware Sensing Coverage in Budget-Constrained Mobile Crowdsensing Networks

Zhang, Maotian; Yang, Panlong; Tian, Chao; Tang, Shaojie; Gao, Xiaofeng; Wang, Zhibo; Xiao, Fu

doi:10.1109/tvt.2015.2490679

Cited by 121 publications

(51 citation statements)

References 38 publications

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“…For this reason, a number of incentive mechanisms have been proposed to increase not only the collected amount of data, but also its quality. Furthermore, a number of frameworks aimed to recruit participants in order to maximize the coverage of the sensing area have been recently proposed [30,61,85,86,104,182,183,208], including detailed reviews of the related literature [58,81,147]. Therefore, in this paper we focus on the QoI Estimation and Enforcement steps of the QoI loop, which has received relatively less attention.…”

Section: A Comprehensive Framework For Qoi In Mobile Crowdsensingmentioning

confidence: 99%

Quality of Information in Mobile Crowdsensing

Restuccia

Ghosh

Bhattacharjee

et al. 2017

ACM Trans. Sen. Netw.

149

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Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work. F. Restuccia et al.Not only are today's smartphones ubiquitous devices, they are also equipped with a plethora of embedded multi-modal sensors; integrate wireless communication technologies such as 4G/WiFi Internet connectivity, and possess complex processing capabilities. For example, the cameras on smartphones can be used as video and image sensors [29], the microphone can be used as an acoustic sensor [39,154,206], and the embedded global positioning system (GPS) receiver can be used to gather accurate location information, while gyroscopes, accelerometers, and proximity sensors can be used to extract contextual information about the users, such as driving or walking states [75,122]. Further, additional sensors can be easily interfaced with smartphones via Bluetooth or wired connections, such as temperature, air quality, and humidity [69,168].These technological features, combined with the advanced sensing capability of humans, has spurred a significant amount of research from both academia and industry, which together have proposed over the last 10 years a myriad of applications based on the emerging mobile crowdsensing paradigm 1 . Mobile crowdsensing empowers ordinary citizens (or users 2 ) with the capability to actively monitor various phenomena pertaining to themselves (e.g., health, social connections) or their community (e.g., environment). This rich information or inference about themselves or the community may also be sent back to the participating or other concerned users to improve their life experiences, thus influencing their choices. Real-life applications, which can take advantage of both low-level sensor data and high-level user activities, range from real-time traffic monitoring applications, to environmental pollution monitoring, crime monitoring, and social networking, just to name a few. For a survey on mobile crowdsensin...

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Section: A Comprehensive Framework For Qoi In Mobile Crowdsensingmentioning

confidence: 99%

Quality of Information in Mobile Crowdsensing

Restuccia

Ghosh

Bhattacharjee

et al. 2017

ACM Trans. Sen. Netw.

149

View full text Add to dashboard Cite

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“…Xiong et al [8] defined a temporal-spatial coverage called -depth coverage and then discussed selection of participants to maximize coverage under the constraints of budget and minimize the budget while satisfying the predefined coverage goal. Zhang et al [9] investigated coverage quality and selected a subset of mobile users to maximize coverage quality under constrained budget. Several researchers considered selecting participants to minimize cost while guaranteeing data quality.…”

Section: Related Workmentioning

confidence: 99%

Multitask Allocation to Heterogeneous Participants in Mobile Crowd Sensing

Zhu

Guo

et al. 2018

Wireless Communications and Mobile Computing

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Task allocation is a key problem in Mobile Crowd Sensing (MCS). Prior works have mainly assumed that participants can complete tasks once they arrive at the location of tasks. However, this assumption may lead to poor reliability in sensing data because the heterogeneity among participants is disregarded. In this study, we investigate a multitask allocation problem that considers the heterogeneity of participants (i.e., different participants carry various devices and accomplish different tasks). A greedy discrete particle swarm optimization with genetic algorithm operation is proposed in this study to address the abovementioned problem. This study is aimed at maximizing the number of completed tasks while satisfying certain constraints. Simulations over a real-life mobile dataset verify that the proposed algorithm outperforms baseline methods under different settings.

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“…[38,39], Zhang et al [40] proposed the maximum coverage quality with a budget constraint problem. They proposed a new algorithm by selecting some of mobile users to maximize the coverage quality.…”

Section: Related Workmentioning

confidence: 99%

PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems

Wang

et al. 2017

Sensors

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Air pollution has become one of the most pressing environmental issues in recent years. According to a World Health Organization (WHO) report, air pollution has led to the deaths of millions of people worldwide. Accordingly, expensive and complex air-monitoring instruments have been exploited to measure air pollution. Comparatively, a vehicle sensing system (VSS), as it can be effectively used for many purposes and can bring huge financial benefits in reducing high maintenance and repair costs, has received considerable attention. However, the privacy issues of VSS including vehicles’ location privacy have not been well addressed. Therefore, in this paper, we propose a new privacy-preserving data aggregation scheme, called PAVS, for VSS. Specifically, PAVS combines privacy-preserving classification and privacy-preserving statistics on both the mean E(·) and variance Var(·), which makes VSS more promising, as, with minimal privacy leakage, more vehicles are willing to participate in sensing. Detailed analysis shows that the proposed PAVS can achieve the properties of privacy preservation, data accuracy and scalability. In addition, the performance evaluations via extensive simulations also demonstrate its efficiency.

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Quality-Aware Sensing Coverage in Budget-Constrained Mobile Crowdsensing Networks

Cited by 121 publications

References 38 publications

Quality of Information in Mobile Crowdsensing

Quality of Information in Mobile Crowdsensing

Multitask Allocation to Heterogeneous Participants in Mobile Crowd Sensing

PAVS: A New Privacy-Preserving Data Aggregation Scheme for Vehicle Sensing Systems

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