A Novel Deep Reinforcement Learning Based Clustering Scheme for WSN

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 co...

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Bibliographic Details
Published inGLOBECOM 2023 - 2023 IEEE Global Communications Conference pp. 2802 - 2807
Main Authors Yan, ChengLong, Deng, YaFeng, Choi, Young-June
Format Conference Proceeding
LanguageEnglish
Published IEEE 04.12.2023
Subjects
clustering
Clustering methods
Energy consumption
Energy efficiency
life time
Power system reliability
Reinforcement learning
reinforcement learning (RL)
Simulation
transmission quality
wireless sensor network (WSN)
Wireless sensor networks
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Summary: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.
ISSN:2576-6813
DOI:10.1109/GLOBECOM54140.2023.10436882