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Incentive Mechanisms for Time Window Dependent Tasks in Mobile Crowdsensing

机译：移动人群感知中时间窗口相关任务的激励机制

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摘要

Mobile crowdsensing can enable numerous attractive novel sensing applications due to the prominent advantages such as wide spatiotemporal coverage, low cost, good scalability, pervasive application scenarios, etc. In mobile crowdsensing applications, incentive mechanisms are necessary to stimulate more potential smartphone users and to achieve good service quality. In this paper, we focus on exploring truthful incentive mechanisms for a novel and practical scenario where the tasks are time window dependent, and the platform has strong requirement of data integrity. We present a universal system model for this scenario based on reverse auction framework and formulate the problem as the problem. We design two incentive mechanisms, and . In single time window case, we design an optimal algorithm based on dynamic programming to select users. Then we determine the payment for each user by auction; while in multiple time window case, we show the general problem is NP-hard, and we design based on greedy approach, which approximates the optimal solution within a factor of , where is the length of sensing time window defined by the platform. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve high computation efficiency, individual rationality and truthfulness.
机译：由于时空覆盖范围广，成本低，可伸缩性好，应用场景无所不在等显着优势，移动人群感知可以启用众多有吸引力的新颖传感应用。在移动人群传感应用中，激励机制对于刺激更多潜在的智能手机用户并实现服务质量好。在本文中，我们专注于探索一种新颖且实用的方案的真实激励机制，该方案的任务与时间窗口相关，并且该平台对数据完整性有强烈要求。我们提出了一种基于逆向拍卖框架的通用场景系统模型，并将问题表述为问题。我们设计了两种激励机制。在单时间窗口的情况下，我们设计了一种基于动态编程的最优算法来选择用户。然后，我们通过拍卖确定每个用户的付款；而在多个时间窗口的情况下，我们发现一般问题是NP难的，并且我们基于贪婪方法进行设计，该方法在的因子内近似最佳解决方案，其中平台定义的感测时间窗口的长度。通过严格的理论分析和广泛的模拟，我们证明了所提出的机制具有较高的计算效率，个人理性和真实性。

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