A Novel Deep Reinforcement Learning Based Clustering Scheme for WSN

ChengLong Yan, Yafeng Deng, Young-June Choi

Abstract: To extend the network's life cycle in wireless sensor networks, clustering plays an important role in balancing energy consumption. In this paper, we propose a novel clustering method based on reinforcement learning that integrates cluster head selection and cluster formation as one step. It considers both energy efficiency and inter cluster interference in the model-free design, thus achieving longer network lifetime and higher quality of packet transmission. To the best of our knowledge, our work is the first paper that integrates cluster head selection and cluster formation using reinforcement learning. Our extensive simulation results show that the proposed method improves the network lifetime by 65% and 29% compared with Low Energy Adaptive Clustering Hierarchy (LEACH) and Greedy Energy Efficient Clustering Scheme (GEECS), respectively, while the data transmission success rate is also increased by 42% and 31%, respectively.