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首页> 外文会议>IEEE International Conference on Communications >Reverse Auction Based Incentive Mechanism for Location-Aware Sensing in Mobile Crowd Sensing
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Reverse Auction Based Incentive Mechanism for Location-Aware Sensing in Mobile Crowd Sensing

机译：移动人群感知中基于逆向拍卖的位置感知感知激励机制

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In mobile crowd sensing (MCS), incentive mechanism is one of the most critical issues, and plays an important role on ensuring the quantity of participants and the coverage rate of sensing task. In this paper, we tackle the problem of stimulating enough smartphone users to join in MCS activities with their smartphones. Moreover, the scenario that the selected users drop out of the sensing tasks with random probability during their sensing processes is also taken into account. Therefore, we propose a novel incentive mechanism called IMRAL - an Incentive Mechanism based on Reverse Auction for Location-aware sensing. IMRAL aims to enhance the participants' enthusiasm by maximizing their expected profits. It consists of two parts: winner selection algorithm and payment determination scheme. In the first part, we formulate a winner selection problem by considering the service coverage of mobile users. Due to the NP-hardness of the problem, we introduce a task-centric method to determine the winning bids with polynomial time complexity. The second part is a payment scheme, which determines the payment to winners by a time proportional share rule to ensure the truthful of IMRAL and consider the effects of the randomness of the selected users, and the winners can obtain the maximum utility. Through rigid theoretical analysis, we demonstrate that the proposed mechanism satisfies the properties of computational efficiency, individual rationality, budget feasibility and truthfulness. Simulation results show that, compared with TRAC (truthful auction for location-aware collaborative sensing in mobile crowdsourcing) and IMC-SS (incentive mechanism for crowdsourcing in the single-requester single-bid-model), the IMRAL can achieve better performance in terms of average user utility and tasks coverage ratio.
机译：在移动人群感知（MCS）中，激励机制是最关键的问题之一，在确保参与者数量和感知任务的覆盖率方面起着重要作用。在本文中，我们解决了刺激足够多的智能手机用户加入其智能手机参加MCS活动的问题。此外，还考虑了所选用户在其感测过程中以随机概率退出感测任务的情况。因此，我们提出了一种新颖的激励机制，称为IMRAL-一种基于反向拍卖的感知位置的激励机制。 IMRAL旨在通过最大程度地提高参与者的预期利润来提高他们的热情。它由两部分组成：获奖者选择算法和付款确定方案。在第一部分中，我们通过考虑移动用户的服务范围来制定赢家选择问题。由于问题的NP难点，我们引入了一种以任务为中心的方法来确定具有多项式时间复杂度的中标价格。第二部分是付款方案，它通过按时间比例分配规则确定向获奖者付款，以确保IMRAL的真实性并考虑所选用户随机性的影响，获奖者可以获得最大效用。通过严格的理论分析，我们证明了所提出的机制满足了计算效率，个人理性，预算可行性和真实性的性质。仿真结果表明，与TRAC（移动众包中的位置感知协作感知的真实拍卖）和IMC-SS（单请求单出价模型中的众包激励机制）相比，IMRAL在性能上可以达到更好的效果。平均用户实用程序和任务覆盖率。

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