Multiagent Actor-Critic Network-Based Incentive Mechanism for Mobile Crowdsensing in Industrial Systems

Mobile crowdsensing (MCS) is an appealing sensing paradigm that leverages the sensing capabilities of smart devices and the inherent mobility of device owners to accomplish sensing tasks with the aim of constructing powerful industrial systems. Incentivizing mobile users (MUs) to participate in sensing activities and contribute high-quality data is of paramount importance to the success of MCS services. In this article, we formulate the competitive interactions between a sensing platform (SP) and MUs as a multistage Stackelberg game with the SP as the leader player and the MUs as the followers. Given the unit prices announced by MUs, the SP calculates the quantity of sensing time to purchase from each MU by solving a convex optimization problem. Then, each follower observes the trading records and iteratively adjusts their pricing strategy in a trial-and-error manner based on a multiagent deep reinforcement learning algorithm. Simulation results demonstrate the efficiency of the proposed method.