With the exponentially increasing number of mobile devices, crowdsensing has been a hot topic to use the available resource of neighbor mobile devices to perform sensing tasks cooperatively. However, there still remain three main obstacles to be solved in the practical system. First, since mobile devices are selfish and rational, it is natural to provide cooperation for sensing with a reasonable payment. Meanwhile, due to the arrival and departure of sensing tasks, resource should be allocated and released dynamically when sensing task comes or leaves. To this end, this paper designs a game theoretic approach based incentive mechanism to encourage the “best” neighbor mobile devices to share their own resource for sensing. Next, in order to adjust resource among mobile devices for the better crowdsensing response, an auction based task migration algorithm is proposed, which can guarantee the truthfulness of announced price of auctioneer, individual rationality, profitability, and computational efficiency. Moreover, taking into account the random movement of mobile devices resulting in the stochastic connection, we also use multi-stage stochastic decision to take posterior resource allocation to compensate for inaccurate prediction. The numerical results show the effectiveness and improvement of the proposed multi-stage stochastic programming based distributed game theoretic methodology (SPG) for crowdsensing.

中文翻译：

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不确定无线场景下人群感知的分布式博弈方法

随着移动设备数量呈指数级增长，利用相邻移动设备的可用资源协同执行传感任务，群体感知已成为一个热门话题。然而，在实际系统中仍然存在三个主要障碍需要解决。首先，由于移动设备是自私和理性的，自然而然地以合理的支付来提供感知的合作。同时，由于感知任务的到来和离开，在感知任务到来或离开时需要动态分配和释放资源。为此，本文设计了一种基于博弈论方法的激励机制，以鼓励“最佳”邻居移动设备共享自己的感知资源。接下来，为了调整移动设备之间的资源以获得更好的群体感应响应，提出了一种基于拍卖的任务迁移算法，可以保证拍卖人公布价格的真实性、个体的合理性、盈利能力和计算效率。此外，考虑到移动设备的随机移动导致随机连接，我们还使用多阶段随机决策来采取后验资源分配来补偿不准确的预测。数值结果显示了所提出的基于多阶段随机规划的分布式博弈论方法（SPG）用于人群感知的有效性和改进。考虑到移动设备的随机移动导致随机连接，我们还使用多阶段随机决策来采取后验资源分配来补偿不准确的预测。数值结果显示了所提出的基于多阶段随机规划的分布式博弈论方法（SPG）用于人群感知的有效性和改进。考虑到移动设备的随机移动导致随机连接，我们还使用多阶段随机决策来采取后验资源分配来补偿不准确的预测。数值结果显示了所提出的基于多阶段随机规划的分布式博弈论方法（SPG）用于人群感知的有效性和改进。