Distributed Vehicle Tracking in Wireless Sensor Network: A Fully Decentralized Multiagent Reinforcement Learning Approach
Teng Liang, Yan Lin, Long Shi, Jun Li, Yijin Zhang, Yuwen Qian
Abstract
Vehicle tracking is one of the important applications of the wireless sensor network (WSN), and sensor scheduling is essential in WSN for achieving an efficient tracking process. Traditional centralized sensor scheduling frameworks cannot meet real-time requirements of vehicle tracking in WSN, because of task overloading caused by limited resource consumption and communication bandwidth. To solve this problem, this letter proposes a multiagent distributed sensor scheduling framework in WSN. This letter first proposes a preactivation-based vehicle tracking model to preactive some sensors in order to reduce unnecessary resource consumption. Then, this letter develops a fully decentralized multiagent reinforcement learning (FDMARL) algorithm to design our multiagent sensor scheduling framework. The simulation results show the convergence and the superiority of our proposed FDMARL-aided sensor scheduling algorithm.