Mobile Crowdsourcing Quality Control Method Based on Four-Party Evolutionary Game in Edge Cloud Environment
Ying Zhao, Yingjie Wang, Peiyong Duan, Haijing Zhang, Zhaowei Liu, Xiangrong Tong, Zhipeng Cai
Abstract
Mobile crowdsourcing (MCS) is a new paradigm that uses various mobile devices to collect sensed data. Mobile edge computing (MEC) can effectively utilize the device resources of mobile edge, greatly relieve the pressure of network bandwidth and improve the response speed. In this article, we construct a four-party evolutionary game model consisting of the platform, crowd workers, task requesters, and edge servers. The computing tasks are conducted on edge servers, which greatly reduce remote data transmission and network operating costs and improve service quality. Taking into account the collusion between the platform and workers, and that between the platform and requesters, we analyze the stability of the strategic equilibrium in MCS using replicator dynamics methods. The optimal payoff strategies of the participants in different initial states are obtained. To prevent cheating and false-reporting problems, reward and punishment strategies are provided. Finally, the stability of the equilibrium of the four-party evolutionary game system is verified by simulation experiments, and an incentive strategy is designed to motivate all parties to choose the trust strategies.