An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network
Article
Kaur, N, Aulakh, IK. (2021). An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network
. 8(31), 1-17. 10.4108/eai.25-2-2021.168808
Kaur, N, Aulakh, IK. (2021). An Energy Efficient Reinforcement Learning Based Clustering Approach for Wireless Sensor Network
. 8(31), 1-17. 10.4108/eai.25-2-2021.168808
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 the research, 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 consumption and 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.
