Abstract
Mobile crowd sensing (MCS) is a novel sensing paradigm which can sense human-centered daily activities and the surrounding environment. The impact of mobility and selfishness of participants on the data reliability cannot be ignored in most mobile crowd sensing systems. To address this issue, we present a universal system model based on the reverse auction framework and formulate the problem as the Multiple Quality Multiple User Selection (MQMUS) problem. The quality-aware incentive mechanism (QAIM) is proposed to meet the quality requirement of data reliability. We demonstrate that the proposed incentive mechanism achieves the properties of computational efficiency, individual rationality, and truthfulness. And meanwhile, we evaluate the performance and validate the theoretical properties of our incentive mechanism through extensive simulation experiments.
Highlights
A new paradigm of sensing with smartphones has emerged which is usually called people-centric mobile sensing or mobile crowd sensing [1]
Research on incentive mechanism has been widely concerned by investigators, and considerable designed schemes about the incentive mechanism design have been put forward which can be classified into nonmonetary incentives [18,19,20] and monetary incentives [21,22,23,24,25,26,27,28,29]
We address the fundamental research issue: how can we achieve high quality crowd sensing with the minimum social cost? To answer this question, we study different conditions of recruiter and candidates in crowd sensing system
Summary
A new paradigm of sensing with smartphones has emerged which is usually called people-centric mobile sensing or mobile crowd sensing [1]. Compared with the traditional sensor networks, MCS is an effective way for large-scale data sensing, processing, and gathering without deploying a large number of sensor nodes. MCS has enabled numerous largescale applications such as urban environment monitoring [2,3,4], traffic flow surveillance [5,6,7], healthcare [8], behavior and relationship discovery [9, 10], indoor localization [11], 3G/WiFi discovering [12,13,14], activity monitoring [15, 16], and bus arrival time prediction [17]. The effect of the aforementioned mobile crowd sensing applications relies heavily on the quantities of participants. Research on incentive mechanism has been widely concerned by investigators, and considerable designed schemes about the incentive mechanism design have been put forward which can be classified into nonmonetary incentives [18,19,20] and monetary incentives [21,22,23,24,25,26,27,28,29]
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