Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88410
Title: A socially-aware incentive mechanism for mobile crowdsensing service market
Authors: Nie, Jiangtian
Xiong, Zehui
Niyato, Dusit
Wang, Ping
Luo, Jun
Keywords: Crowdsensing
Social Network Effects
Engineering::Computer science and engineering
Issue Date: 2019
Source: Nie, J., Xiong, Z., Niyato, D., Wang, P., & Luo, J. (2018). A socially-aware incentive mechanism for mobile crowdsensing service market. IEEE Global Communications Conference. doi:10.1109/GLOCOM.2018.8647726
Abstract: Mobile Crowdsensing has shown a great potential to address large-scale problems by allocating sensing tasks to pervasive Mobile Users (MUs). The MUs will participate in a Crowdsensing platform if they can receive satisfactory reward. In this paper, in order to effectively and efficiently recruit sufficient MUs, i.e., participants, we investigate an optimal reward mechanism of the monopoly Crowdsensing Service Provider (CSP). We model the rewarding and participating as a two-stage game, and analyze the MUs' participation level and the CSP's optimal reward mechanism using backward induction. At the same time, the reward is designed taking the underlying social network effects amid the mobile social network into account, for motivating the participants. Namely, one MU will obtain additional benefits from information contributed or shared by local neighbours in social networks. We derive the analytical expressions for the discriminatory reward as well as uniform reward with complete information, and approximations of reward incentive with incomplete information. Performance evaluation reveals that the network effects tremendously stimulate higher mobile participation level and greater revenue of the CSP. In addition, the discriminatory reward enables the CSP to extract greater surplus from this Crowdsensing service market.
URI: https://hdl.handle.net/10356/88410
http://hdl.handle.net/10220/49197
DOI: https://doi.org/10.1109/GLOCOM.2018.8647726
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/GLOCOM.2018.8647726
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers
ERI@N Conference Papers
IGS Conference Papers
SCSE Conference Papers

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