2013/10/21 by Jiajun Sun, Sun, Jiajun
Computer Science · Decision Sciences · #Auction Theory and Applications #Computer Science and Game Theory (cs.GT) #FOS: Computer and information sciences #Mobile Crowdsensing and Crowdsourcing #Networking and Internet Architecture (cs.NI) #Privacy-Preserving Technologies in Data
paper · pdf · doi:10.48550/arxiv.1310.5485
openalex publication_date 2013/10/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Crowd sensing is a new paradigm which leverages the ubiquity of sensor-equipped mobile devices to collect data. To achieve good quality for crowd sensing, incentive mechanisms are indispensable to attract more participants. Most of existing mechanisms focus on the expected utility prior to sensing, ignoring the risk of low quality solution and privacy leakage. Traditional incentive mechanisms such as the Vickrey-Clarke-Groves (VCG) mechanism and its variants are not applicable here. In this paper, to address these challenges, we propose a behavior based incentive mechanism for crowd sensing applications with budget constraints by applying sequential all-pay auctions in mobile social networks (MSNs), not only to consider the effects of extensive user participation, but also to maximize high quality of the context based sensing content submission for crowd sensing platform under the budget constraints, where users arrive in a sequential order. Through an extensive simulation, results indicate that incentive mechanisms in our proposed framework outperform the best existing solution.