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Issue No. 01 - Jan. (2016 vol. 65)
ISSN: 0018-9340
pp: 294-307
Kai Han , School of Computer Science and Technology/Suzhou Institute for Advanced Study, University of Science and Technology of China, China
Chi Zhang , School of Computer Engineering, Nanyang Technological University, Singapore
Jun Luo , School of Computer Engineering, Nanyang Technological University, Singapore
Menglan Hu , School of Computer Engineering, Nanyang Technological University, Singapore
Bharadwaj Veeravalli , Department of Electrical and Computer Engineering, National University of Singapore, Singapore
Mobile crowdsensing leverages mobile devices (e.g., smart phones) and human mobility for pervasive information exploration and collection; it has been deemed as a promising paradigm that will revolutionize various research and application domains. Unfortunately, the practicality of mobile crowdsensing can be crippled due to the lack of incentive mechanisms that stimulate human participation. In this paper, we study incentive mechanisms for a novel Mobile Crowdsensing Scheduling (MCS) problem, where a mobile crowdsensing application owner announces a set of sensing tasks, then human users (carrying mobile devices) compete for the tasks based on their respective sensing costs and available time periods, and finally the owner schedules as well as pays the users to maximize its own sensing revenue under a certain budget. We prove that the MCS problem is NP-hard and propose polynomial-time approximation mechanisms for it. We also show that our approximation mechanisms (including both offline and online versions) achieve desirable game-theoretic properties, namely truthfulness and individual rationality, as well as $_$\mathcal {O}(1)$_$ performance ratios. Finally, we conduct extensive simulations to demonstrate the correctness and effectiveness of our approach.
Sensors, Mobile communication, Schedules, Approximation algorithms, Algorithm design and analysis, Approximation methods, Mobile handsets

K. Han, C. Zhang, J. Luo, M. Hu and B. Veeravalli, "Truthful Scheduling Mechanisms for Powering Mobile Crowdsensing," in IEEE Transactions on Computers, vol. 65, no. 1, pp. 294-307, 2016.
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