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A Novel Deep Reinforcement Learning Based Clustering Scheme for WSN
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Publication Year
2023-01-01
Journal
Proceedings - IEEE Global Communications Conference, GLOBECOM
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - IEEE Global Communications Conference, GLOBECOM, pp.2802-2807
Keyword
clusteringlife timereinforcement learning (RL)transmission qualitywireless sensor network (WSN)
Mesh Keyword
Cluster formationsCluster-head selectionsClustering schemeClusteringsLife-timesNetwork lifetimeReinforcement learningReinforcement learningsTransmission qualityWireless sensor network
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Networks and CommunicationsHardware and ArchitectureSignal Processing
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.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36944
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85187377363&origin=inward
DOI
https://doi.org/10.1109/globecom54140.2023.10436882
Journal URL
https://ieeexplore.ieee.org/xpl/conhome/1000308/all-proceedings
Type
Conference
Funding
VII. ACKNOWLEDGEMENT This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2023R1A2C1003783) and the BK21 FOUR program of the National Research Foundation of Korea funded by the Ministry of Education (NRF5199991014091).
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Choi, Youngjune최영준
Department of Software and Computer Engineering
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