Wireless power transfer (WPT) is a promising technology to prolong the lifetime of the sensors and communication devices, i.e., workers, in completing crowdsourcing tasks by providing continuous and cost-effective energy supplies. In this paper, we propose a wireless powered spatial crowdsourcing framework which consists of two mutually dependent phases: task allocation phase and data crowdsourcing phase. In the task allocation phase, we propose a Stackelberg game based mechanism for the spatial crowdsourcing platform to efficiently allocate spatial tasks and wireless charging power to each worker. In the data crowdsourcing phase, the workers may have an incentive to misreport its real working location to improve its utility, which causes adverse effects to the spatial crowdsourcing platform. To address this issue, we present three strategyproof deployment mechanisms for the spatial crowdsourcing platform to place a mobile base station, e.g., vehicle or robot, which is responsible for transferring the wireless power and collecting the crowdsourced data. As the benchmark, we first apply the classical median mechanism and evaluate its worst-case performance. Then, we design a conventional strategyproof deployment mechanism to improve the expected utility of the spatial crowdsourcing platform under the condition that the workers' locations follow a known geographical distribution. For a more general case with only the historical location data available, we propose a deep learning based strategyproof deployment mechanism to maximize the spatial crowdsourcing platform's utility. Extensive experimental results based on synthetic and real-world datasets reveal the effectiveness of the proposed framework in allocating tasks and charging power to workers while avoiding the dishonest worker's manipulation.

中文翻译：

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无线供电空间众包网络的机制设计

无线电力传输 (WPT) 是一项很有前途的技术，可通过提供连续且具有成本效益的能源供应来延长传感器和通信设备（即工人）在完成众包任务时的使用寿命。在本文中，我们提出了一个无线供电的空间众包框架，它由两个相互依赖的阶段组成：任务分配阶段和数据众包阶段。在任务分配阶段，我们为空间众包平台提出了一种基于 Stackelberg 博弈的机制，以有效地为每个工人分配空间任务和无线充电功率。在数据众包阶段，工作人员可能有动机误报其真实工作位置以提高其效用，这对空间众包平台造成不利影响。为了解决这个问题，我们提出了空间众包平台的三种策略性部署机制，用于放置移动基站，例如车辆或机器人，负责传输无线电力并收集众包数据。作为基准，我们首先应用经典中值机制并评估其最坏情况的性能。然后，我们设计了一种传统的策略证明部署机制，以在工人位置遵循已知地理分布的情况下提高空间众包平台的预期效用。对于只有历史位置数据可用的更一般情况，我们提出了一种基于深度学习的策略证明部署机制，以最大化空间众包平台的效用。