NDLI: Mobile crowdsensing game in vehicular networks

Content Provider	IEEE Xplore Digital Library
Author	Liang Xiao Tianhua Chen Caixia Xie Jinliang Liu
Copyright Year	2015
Description	Author affiliation: Dept. Commun. Eng., Xiamen Univ., Xiamen, China (Liang Xiao; Tianhua Chen; Caixia Xie; Jinliang Liu)
Abstract	Vehicular crowdsensing takes advantage of the mobility of vehicles to provide location-based services in large-scale areas. In this paper, we analyze vehicular crowdsensing and formulate the interactions between a crowdsensing server and a number of vehicles equipped with sensors in the area of interest as a vehicular crowdsensing game. Each participant vehicle chooses its sensing strategy based on the sensing and transmission costs, and the expected payment by the server, while the server determines its payment policy according to the number and accuracy of the sensing reports. A reinforcement learning based crowdsensing strategy is proposed for vehicular networks, with incomplete system parameters such as the sensing costs of the other vehicles. The server and vehicles achieve their optimal payment and sensing strategies by learning via trials, respectively. Simulation results have verified the efficiency of the proposed mobile crowdsensing systems, showing that the average utilities of the vehicles and the server can be improved and converged to the optimal values in fast speed. Vehicles with less sensing costs are motivated to upload more accurate sensing data.
Starting Page	1
Ending Page	6
File Size	238178
Page Count	6
File Format	PDF
ISBN	9781479986392
ISSN	21593450
e-ISBN	9781479986415
DOI	10.1109/TENCON.2015.7372721
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-11-01
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Sensors Vehicles Servers Games Mobile communication Monitoring Smart phones Q-learning Vehicular networks mobile crowdsensing game theory
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Computer Science Applications

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