Mobile Crowdsensing Games in Vehicular Networks
Mobile Crowdsensing Games in Vehicular Networks
复制标题
车载网络中的移动群智游戏
DOI：
10.1109/tvt.2016.2647624
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发表时间：
2017-01
期刊：
影响因子：
--
通讯作者：
H. V. Poor
中科院分区：
文献类型：
--
作者：
T. Chen;C. Xie;H. Dai;H. V. Poor
车辆群体感知利用车辆的移动性在大范围内提供基于位置的服务。本文分析了车载网络中的移动众感（MCS），并将众感服务器与在感兴趣区域内配备传感器的车辆之间的交互表述为车载众感博弈。每个参与车辆根据感知成本、无线信道状态和期望支付来选择自己的感知策略。MCS服务器评估每个传感报告的准确性，并相应地支付车辆费用。推导了静态车辆群体感知博弈中累积感知任务和最优感知任务的纳什均衡，体现了感知精度与MCS服务器总体支付之间的权衡。针对动态车辆众感博弈，提出了一种基于q学习的MCS支付策略和感知策略，并采用决策后状态学习技术，利用已知的无线信道模型加快每辆车的学习速度。基于马尔可夫链通道模型的仿真验证了所提出的MCS系统的效率，结果表明，该系统在平均效用、传感精度和车辆能耗方面都优于基准MCS系统。
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.
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DOI：
10.1109/infocom.2015.7218592
发表时间：
2015-04
期刊：
2015 IEEE Conference on Computer Communications (INFOCOM)
影响因子：
--
作者：
Yue-wei Wei;Yanmin Zhu;Hongzi Zhu;Qian Zhang;Guangtao Xue
通讯作者：
Yue-wei Wei;Yanmin Zhu;Hongzi Zhu;Qian Zhang;Guangtao Xue
影响因子：
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作者：
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影响因子：
5.4
作者：
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通讯作者：
Nicholas Mastronarde;M. Schaar
影响因子：
11.2
作者：
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通讯作者：
Lei, Hui
DOI：
10.1016/j.procs.2012.06.165
发表时间：
2012
期刊：
--
影响因子：
--
作者：
Kun-Chan Lan;Chien-Ming Chou;Han-Yi Wang
通讯作者：
Kun-Chan Lan;Chien-Ming Chou;Han-Yi Wang