Exploiting Multi-Dimensional Task Diversity in Distributed Auctions for Mobile Crowdsensing
Exploiting Multi-Dimensional Task Diversity in Distributed Auctions for Mobile Crowdsensing
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DOI：
10.1109/tmc.2020.2987881
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发表时间：
2021-08-01
影响因子：
7.9
通讯作者：
Li, Wei
中科院分区：
文献类型：
--
作者：
Cai, Zhipeng;Duan, Zhuojun;Li, Wei
为了促进移动众感系统（mcs）的发展，已经提出了许多拍卖方案来激励移动用户的参与。但是，大多数现有的研究并没有充分探讨MCSs的任务多样性。为了进一步挖掘任务多样性，提高MCSs的性能，本文在考虑部分履行、双边多任务、属性多样性和价格多样性等多维任务多样性的情况下，研究了感知任务分配和调度的联合问题。首先，建立了以任务所有者为中心的拍卖模型，并提出了两种分布式拍卖方案（CPAS和TPAS），使每个任务所有者都能在本地处理拍卖过程。然后，建立了以移动用户为中心的拍卖模型，并开发了VPAS和DPAS两种分布式拍卖方案，以方便本地拍卖的实施。这四种拍卖方案在确定获胜者和计算付款的方法上有所不同。我们进一步严格证明了所有四种拍卖方案（CPAS， TPAS， VPAS和DPAS）都具有计算效率，个体理性和激励相容，并且CPAS和TPAS都是预算可行的。最后，我们通过与最新的实际数据实验进行比较，综合评估了CPAS、TPAS、VPAS和DPAS的有效性。
To promote development of Mobile Crowdsensing Systems (MCSs), numerous auction schemes have been proposed to motivate mobile users' participation. But, task diversity of MCSs has not been fully explored by most existing works. To further exploit task diversity and improve performance of MCSs, in this paper, we investigate the joint problem of sensing task assignment and schedule with considering multi-dimensional task diversity, including partial fulfillment, bilaterally-multi-schedule, attribute diversity, and price diversity. First, task owner-centric auction model is formulated and two distributed auction schemes (CPAS and TPAS) are proposed such that each task owner can locally process auction procedure. Then, mobile user-centric auction model is established and two distributed auction schemes (VPAS and DPAS) are developed to facilitate local auction implementation. These four auction schemes differ in their approaches to determine winners and compute payments. We further rigorously prove that all the four auction schemes (CPAS, TPAS, VPAS, and DPAS) are computationally-efficient, individually-rational, and incentive-compatible and that both CPAS and TPAS are budget-feasible. Finally, we comprehensively evaluate the effectiveness of CPAS, TPAS, VPAS, and DPAS via comparing with the state-of-the-art in real-data experiments.