Abstract
Incentive mechanisms are pivotal in encouraging mobile users to participate to contribute their sensing information. However, most studies on incentive mechanisms merely considered individual behaviors of the users rather than their interdependency. The interdependent behaviors of the users are common as they originate from the social network effects that exist in the underlying mobile social domain. For example, a user from a crowdsensing-based traffic condition application can obtain a more accurate traffic mapping if other users share their road traffic information. Moreover, the incomplete information problem is also a critical but open issue in the real-life applications of crowdsensing. To address these issues, we propose a novel incentive mechanism considering both the social network effects and the incomplete information situation. In particular, we develop a Bayesian Stackelberg game, and study the participation strategies of users as well as the incentive mechanism through backward induction method. We then analytically prove that the Bayesian Stackelberg equilibrium is uniquely determined. Moreover, the numerical results are provided to evaluate the proposed socially-aware incentive mechanisms.
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Acknowledgement
This work is supported by the National Research Foundation (NRF), Singapore, under Singapore Energy Market Authority (EMA), Energy Resilience, NRF2017EWT-EP003-041, Singapore NRF2015-NRF-ISF001-2277, Singapore NRF National Satellite of Excellence, Design Science and Technology for Secure Critical Infrastructure NSoE DeST-SCI2019-0007, A*STAR-NTU-SUTD Joint Research Grant on Artificial Intelligence for the Future of Manufacturing RGANS1906, Wallenberg AI, Autonomous Systems and Software Program and Nanyang Technological University (WASP/NTU) under grant M4082187 (4080). This work is also supported in part by National Natural Science Foundation of China (Grant No. 51806157).
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Nie, J., Luo, J., Xiong, Z., Niyato, D., Wang, P., Zhang, Y. (2020). Incentive Mechanism for Socially-Aware Mobile Crowdsensing: A Bayesian Stackelberg Game. In: Yu, D., Dressler, F., Yu, J. (eds) Wireless Algorithms, Systems, and Applications. WASA 2020. Lecture Notes in Computer Science(), vol 12384. Springer, Cham. https://doi.org/10.1007/978-3-030-59016-1_33
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DOI: https://doi.org/10.1007/978-3-030-59016-1_33
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