Exploiting Multi-Dimensional Task Diversity in Distributed Auctions for Mobile Crowdsensing

Zhipeng Cai

doi:10.1109/TMC.2020.2987881

2021-08-01

Zhipeng Cai (Zhipeng Cai)

Zhuojun Duan

Wei Li *

DOI：10.1109/TMC.2020.2987881

原文链接

查看原文

摘要

En 中文

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.

Keyword:

Task analysis

Sensors

Crowdsensing

Schedules

Cloud computing

Games

Mobile computing

Mobile crowdsensing system

truthful auction

task assignment

task schedule