Mean-Field-Game-Based Dynamic Task Pricing in Mobile Crowdsensing

Abstract

Mobile crowdsensing (MCS) is an effective perception paradigm for large-scale tasks, driven by the proliferation of mobile devices with more powerful sensing and computing capabilities. An effective incentive mechanism is critical to the operation of an MCS system in promoting public engagement. However, the great majority of works discuss fixed task pricing, while the inherent inequality of the supply–demand relationship of the tasks exists. Therefore, it is essential to study the dynamic task pricing problem in the peer-to-peer data sharing MCS system. In this article, we formulate the interactions between the requester and the sensors as a two-stage Stackelberg differential game model, while considering the average behavior of sensors to solve the dynamic task pricing problem. Specifically, in the game model, the requester is the leader who first announces the issued task rate and provides decisive state-changing task pricing dynamics to the sensors. Then, the sensors are the followers who decide the rate of tasks completed noncooperatively based on requesters’ observed strategy, using the level of effort as the state dynamics. The requester and the sensors interact through a mean-field term included in the dynamic state functions, which catches the average behavior of all users. By solving the model, the optimal strategies for the users and the optimal tasks pricing trends in the dynamic environment are obtained. Furthermore, the effectiveness and feasibility of the scheme are verified by a series of numerical simulation experiments.