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Journal of Sensors
Volume 2017 (2017), Article ID 5757125, 14 pages
Research Article

Quality-Aware Incentive Mechanism for Mobile Crowd Sensing

1College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, Jiangsu 210003, China
2Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing, Jiangsu 210003, China
3Key Lab of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Ministry of Education, Nanjing, Jiangsu 210003, China

Correspondence should be addressed to Hai-ping Huang; nc.ude.tpujn@phh

Received 4 March 2017; Revised 24 July 2017; Accepted 22 August 2017; Published 28 September 2017

Academic Editor: Javier Sedano

Copyright © 2017 Ling-Yun Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.