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Quality-Driven Online Task-Bundling-Based Incentive Mechanism for Mobile Crowdsensing
IEEE Transactions on Vehicular Technology ( IF 6.8 ) Pub Date : 2022-04-26 , DOI: 10.1109/tvt.2022.3170505
Cheng Li , Guoliang Ji , Zheng Yao , Baoxian Zhang

With the advancement of wireless communication technologies and rich embedded sensors in smart mobile devices, mobile crowdsensing has become an attractive paradigm for collecting sensing data from surrounding environments. Effective incentive mechanisms often play an important role in guaranteeing high quality of services and wide involvement of workers. Much work has been carried out in this aspect to encourage workers to strenuously and truthfully provide high quality data. However, task quality does not only depend on workers’ subjective effort or truthfulness, but also workers’ distribution and task quality growth law. In this paper, we consider a scenario where worker arrivals are unevenly distributed and the growth of task quality conforms to the law of diminishing margin of workers’ efforts. We propose a Quality-driven Online Task-Bundling-based incentive mechanism (QOTB). The design objective is to maximize the social welfare while maximally satisfying the task quality requirements. In QOTB, we introduce Mental accounting Theory to build accounts for task execution profit and bonus, respectively, which are then used to derive the participation willingness of workers. We adopt task bundling to stimulate workers to change their original travel schedules for balancing the task participations according to the popularities of task locations and also the traveling cost. We present the detailed mechanism design of QOTB. We prove that the QOTB mechanism has desired properties of willingness truthfulness, individual rationality, and computation efficiency. We conduct extensive simulations and the results show that QOTB can effectively improve the social welfare while satisfying the task quality requirements.

中文翻译:

质量驱动的基于在线任务捆绑的移动众感激励机制

随着无线通信技术的进步和智能移动设备中丰富的嵌入式传感器,移动人群感知已成为从周围环境中收集感知数据的有吸引力的范例。有效的激励机制往往在保证服务质量和工人广泛参与方面发挥重要作用。在这方面已经开展了大量工作,以鼓励工人努力如实提供高质量的数据。但是,任务质量不仅取决于工人的主观努力或真实性,还取决于工人的分配和任务质量的增长规律。在本文中,我们考虑了工人到达分布不均且任务质量的增长符合工人努力边际递减规律的场景。我们提出了一种质量驱动的基于在线任务捆绑的激励机制(QOTB)。设计目标是在最大限度满足任务质量要求的同时,实现社会福利最大化。在 QOTB 中,我们引入心理核算理论,分别建立任务执行利润和奖金的账户,然后用于推导工人的参与意愿。我们采用任务捆绑来刺激工人改变他们原来的旅行时间表,以根据任务地点的受欢迎程度和旅行成本来平衡任务参与。我们介绍了 QOTB 的详细机制设计。我们证明了 QOTB 机制具有意愿真实性、个体理性和计算效率的期望属性。
更新日期:2022-04-26
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