Research Article
An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network
@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}, volume={8}, number={31}, 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
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.
Copyright © 2021 Navpreet Kaur et al., licensed to EAI. This is an open-access article distributed under the terms of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
