Charging Efficiency Optimization Based on Swarm Reinforcement Learning under Dynamic Energy Consumption for WRSN

Wireless rechargeable sensor networks (WRSN) have been widely used to solve the energy constraint problem of wireless sensor networks (WSN). Improving the charging efficiency of the mobile charger is crucial for WRSN. However, the time-varying states of WRSN caused by dynamic changes in node energy consumption during the charging process of the mobile charger make the optimization of charging efficiency rather difficult in WRSN. To solve this problem, a kind of optimization approach based on swarm reinforcement learning is presented in this paper. The presented approach lets the multiple agents better adapt to the dynamic energy distribution in WRSN by designing a dynamic energy consumption model. Then, it utilizes Rank-Based Ant System to ensure that the mobile charger gets the initial optimal requesting sensor nodes, which contributes to accelerating the convergence speed of swarm reinforcement learning. Finally, it adopts particle swarm optimization to improve the learning effectiveness during the exchanges of experience among multiple agents, which contributes to optimizing the charging path of MC. Extensive simulations show that the presented approach achieves better charging performance than the existing typical approaches, and it has significant advantages in terms of charging efficiency, sensor node dead ratio, and mobile charger energy efficiency ratio.