Attention Mechanism and Value Reinforcement Learning Based One-to-Multiple Charging Scheduling Scheme In WRSN
In large-scale wireless rechargeable sensor networks ( WRSNs ),the one-to-one charging mode can hardly meet the huge energy demand of nodes,and the one-to-multiple charging mode with higher charging efficiency becomes a more reasonable choice.An online one-to-multiple charging scheduling scheme( MAQRL) for WRSNs based on attention mechanism and value learning is proposed to optimize the mobile charging device( MC) scheduling in terms of charging sequence and charging time.Firstly,the nodes in the net-work are clustered based on the MC effective charging range covering the most nodes,and the charging sequence is optimized based on value reinforcement learning.By combining attention mechanism and value reinforcement learning,MAQRL uses attention mechanism to extract features and MC's attention to nodes,and uses double value reinforcement learning to reduce overestimation,to improve the char-ging performance of the charging scheme.Secondly,by analyzing the average remaining living time of nodes in the whole network and the average movement delay of MC,the charging time is dynamically optimized to reduce the death of subsequent nodes caused by too long waiting time.Extensive simulation experiments show that MAQRL has better performance in reducing node mortality and charging delay compared with several existing charging schemes.
万方数据
维普
