Mobile Crowdsensing Games in Vehicular Networks

被引:104
作者
Xiao, Liang [1 ]
Chen, Tianhua [1 ]
Xie, Caixia
Dai, Huaiyu [2 ]
Poor, H. Vincent [3 ]
机构
[1] Xiamen Univ, Dept Commun Engn, Xiamen 361005, Peoples R China
[2] North Carolina State Univ, Dept Elect & Comp Engn, Raleigh, NC 27695 USA
[3] Princeton Univ, Dept Elect Engn, Princeton, NJ 08544 USA
基金
美国国家科学基金会; 中国国家自然科学基金;
关键词
Game theory; mobile crowdsensing (MCS); reinforcement learning; vehicular networks; EFFICIENT; TASKS;
D O I
10.1109/TVT.2016.2647624
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Vehicular crowdsensing takes advantage of the mobility of vehicles to provide location-based services in large-scale areas. In this paper, mobile crowdsensing (MCS) in vehicular networks is analyzed and the interactions between a crowdsensing server and vehicles equipped with sensors in an area of interest is formulated as a vehicular crowdsensing game. Each participating vehicle chooses its sensing strategy based on the sensing cost, radio channel state, and the expected payment. The MCS server evaluates the accuracy of each sensing report and pays the vehicle accordingly. The Nash equilibrium of the static vehicular crowdsensing game is derived for both accumulative sensing tasks and best-quality sensing tasks, showing the tradeoff between the sensing accuracy and the overall payment by the MCS server. A Q-learning-based MCS payment strategy and sensing strategy is proposed for the dynamic vehicular crowdsensing game, and a postdecision state learning technique is applied to exploit the known radio channel model to accelerate the learning speed of each vehicle. Simulations based on a Markov-chain channel model are performed to verify the efficiency of the proposed MCS system, showing that it outperforms the benchmark MCS system in terms of the average utility, the sensing accuracy, and the energy consumption of the vehicles.
引用
收藏
页码:1535 / 1545
页数:11
相关论文
共 33 条
[21]  
Uddin M. Y. S., 2011, Proceedings of the 2011 IEEE 32nd Real-Time Systems Symposium (RTSS 2011), P317, DOI 10.1109/RTSS.2011.36
[22]  
Wang XL, 2013, IEEE INFOCOM SER, P2517
[23]  
Weaver A. C., 2012, COMP COMM NETW ICCCN, P1, DOI 10.1109/ICCCN.2012.6289256
[24]   Quality-Driven Auction-Based Incentive Mechanism for Mobile Crowd Sensing [J].
Wen, Yutian ;
Shi, Jinyu ;
Zhang, Qi ;
Tian, Xiaohua ;
Huang, Zhengyong ;
Yu, Hui ;
Cheng, Yu ;
Shen, Xuemin .
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2015, 64 (09) :4203-4214
[25]  
Xiang Zhang, 2015, 2015 IEEE Conference on Computer Communications (INFOCOM). Proceedings, P2830, DOI 10.1109/INFOCOM.2015.7218676
[26]  
Xiao L, 2015, IEEE ICC, P7157, DOI 10.1109/ICC.2015.7249468
[27]  
Xiaofan He, 2015, 2015 IEEE Conference on Computer Communications (INFOCOM). Proceedings, P1787, DOI 10.1109/INFOCOM.2015.7218560
[28]  
Yang DJ, 2012, MOBICOM 12: PROCEEDINGS OF THE 18TH ANNUAL INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND NETWORKING, P173
[29]  
Yang XF, 2015, PROCEEDINGS OF 2015 IEEE 12TH INTERNATIONAL CONFERENCE ON ELECTRONIC MEASUREMENT & INSTRUMENTS (ICEMI), VOL. 1, P1, DOI 10.1109/ICEMI.2015.7494169
[30]  
Yu Hua, 2015, 2015 IEEE Conference on Computer Communications (INFOCOM). Proceedings, P1616, DOI 10.1109/INFOCOM.2015.7218541