sis 18: e6

Research Article

An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network

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  • @ARTICLE{10.4108/eai.25-2-2021.168808,
        author={Navpreet Kaur and Inderdeep Kaur Aulakh},
        title={An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network},
        journal={EAI Endorsed Transactions on Scalable Information Systems: Online First},
        volume={},
        number={},
        publisher={EAI},
        journal_a={SIS},
        year={2021},
        month={2},
        keywords={Wireless Sensor Network (WSN), Clustering, Reinforcement Learning, Energy Consumption, Network Lifetime},
        doi={10.4108/eai.25-2-2021.168808}
    }
    
  • Navpreet Kaur
    Inderdeep Kaur Aulakh
    Year: 2021
    An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network
    SIS
    EAI
    DOI: 10.4108/eai.25-2-2021.168808
Navpreet Kaur1,*, Inderdeep Kaur Aulakh2
  • 1: Research Scholar, Punjab University, UIET & Assistance Professor, Chandigarh University
  • 2: Associate Professor, Punjab University
*Contact email: navpreetkaur.sm@yahoo.com

Abstract

Clustering is known to conserve energy and enhance the network lifetime of Wireless Sensor Network (WSN). Although, the topic of energy efficiency has been well researched in conventional WSN, but it has not been extensively studied. In theresearch, Reinforcement Learning (RL) based energy-aware clustering algorithm is proposed by which the neighboring nodes in the cluster selects an appropriate Cluster Head ( CH) by observing the environmental conditions like as energy consumptionand coverage that is computed as distance from the CH to the Base Station (BS). An optimal cluster is selected by each neighboring node, which minimized the energy consumption and network lifetime. The problem of selecting an optimal CH is resolved using the RL approach. Using the RL approach, the CH having the highest reward point is selected for data communication. The results show that energy saving of 7.41%, 3.27%, 4.03%, and 2.79 % is achieved for 100, 200, 300, and 400 deployed nodes, respectively.