Wireless sensor network clustering algorithm based on deep reinforcement learning
In view of the frequent topology changes in wireless sensor networks challenge the stability of clustering,which leads to data transmission redundancy and energy consumption surge,a clustering algorithm based on deep reinforcement learning is proposed.By integrating energy,position and density factors,deep reinforcement learning combined with sensor energy model is used to accurately cluster nodes and optimize inter-cluster paths to achieve efficient clustering strategies.Simulation results show that compared with the comparison method,the research algorithm can significantly improve the network lifetime by about 40%,effectively curb energy consumption,and significantly extend the life cycle of wireless sensor networks,demonstrating its superior performance and practical value in complex environments.
deep reinforcement learningwireless sensornetwork clusteringnode trust degreedata clustering
万方数据
维普
