Thanos: Incentive Mechanism with Quality Awareness for Mobile Crowd Sensing
Thanos: Incentive Mechanism with Quality Awareness for Mobile Crowd Sensing
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DOI：
10.1109/tmc.2018.2868106
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发表时间：
2019-08-01
影响因子：
7.9
通讯作者：
Xu, Jinhui
中科院分区：
文献类型：
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作者：
Jin, Haiming;Su, Lu;Xu, Jinhui
近年来，出现了移动人群感知(MCS)系统。它们利用配备了各种移动设备的公共人群来执行大规模的传感任务。在本文中，我们研究了MCS系统中的一个关键问题，即激励工人参与。与现有的工作不同，我们提出了一个名为Thanos的MCS系统激励框架，其中包含了一个关键的衡量标准，称为工人的信息质量(QOI)。由于各种因素(如传感器质量和环境噪声)，每个工人贡献的感官数据的质量差异很大。以较少的费用获得高质量的数据一直是MCS平台的理想。从技术上讲，我们的Thanos设计是基于反向组合拍卖。我们研究了单目标和多目标的组合拍卖模型。对于前者，我们设计了一种真实的、个人理性的、计算高效的机制，以确保接近最优的社会福利。对于后者，我们设计了一种满足个体理性和计算效率的迭代下降机制，并在保证逼近比的情况下近似最大化社会福利。通过大量的仿真，我们验证了我们对Thanos所保证的各种理想性质的理论分析。
Recent years have witnessed the emergence of mobile crowd sensing (MCS) systems. which leverage the public crowd equipped with various mobile devices for large scale sensing tasks. In this paper, we study a critical problem in MCS systems, namely, incentivizing worker participation. Different from existing work, we propose an incentive framework for MCS systems, named Thanos, that incorporates a crucial metric, called workers' quality of information (QoI). Due to various factors (e.g., sensor quality and environment noise), the quality of the sensory data contributed by individual workers varies significantly. Obtaining high quality data with little expense is always the ideal of MCS platforms. Technically, our design of Thanos is based on reverse combinatorial auctions. We investigate both the single- and multi-minded combinatorial auction models. For the former, we design a truthful, individual rational, and computationally efficient mechanism that ensures a close-to-optimal social welfare. For the latter, we design an iterative descending mechanism that satisfies individual rationality and computational efficiency, and approximately maximizes the social welfare with a guaranteed approximation ratio. Through extensive simulations, we validate our theoretical analysis on the various desirable properties guaranteed by Thanos.