Research on Charging Strategy of Wireless Sensor Network Based on Reinforcement Learning
The charging strategy based on Mobile Charger(MC)has always been the research hotspot of Wireless Rechargeable Sensor Networks(WRSN).The existing charging strategy generally makes the selection of charging nodes in real-time according to the local network information.The lack of global information makes the fairness of the charging strategy unable to be guaranteed,resulting in some nodes being unable to be charged in time.To solve this problem,a novel on-demand charging strategy is designed.Firstly,considering the significant difference in the energy consumption rate of nodes,a calculation method for the remaining life of nodes is proposed.A dual-Q learning frame-work is developed to optimize the charging strategy,named Q-Charge charging strategy.To speed up the learning speed of agents in this charging strategy,a heuristic learning strategy is introduced into the agent learning strategy to replace random action selection with purposeful action selection.The simulation results show that the Q-Charge char-ging strategy not only improves the charging efficiency but also speeds up the convergence speed of the MC moving path.
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