A Crowdsensing Service Pricing Method in Vehicular Edge Computing

Zheng Li, Sizhe Tang, Hao Tian, Haolong Xiang, Xiaolong Xu, Wanchun Dou

Abstract: The rapid advancements in vehicular networking technology have enabled onboard users to access a variety of emerging services like precise navigation and real-time hazard avoidance. However, as the diversity and volume of required data expand and demands intensify, most of vehicular services are unable to satisfy user expectations. Although crowdsensing architectures based on edge computing have been proposed in vehicular networks, how to optimally allocate the sensing service time of roadside units and set appropriate prices for services to maximize user benefits still remain significant challenges. To solve the above issue, in this paper, a crowdsensing service pricing method is proposed in vehicular edge computing. Specifically, a vehicular networking crowdsensing framework based on the edge computing is designed. Then, the optimization problem of perception time pricing during the crowdsensing process is modeled into a Stackelberg game and further the multi-agent deep deterministic policy gradient-based algorithm is employed to obtain the optimal pricing strategy with user profits maximization. Finally, simulation results demonstrate that the proposed method significantly increases the payoff for all participants during the crowdsensing process.

External IDs:dblp:conf/ispa/LiTTX0D24