Energy Efficient Cluster Head Selection Using Hybrid RL-PSO Approach

 

Arpita Choudhary 1,*, N. C. Barwar 2, Vikas Chouhan3

 

1,2 Department of Computer science and Engineering, MBM University Jodhpur, India

3Canadian Institute for Cybersecurity, NB, Canada

Emails: erarpita@gmail.com, ncbarwar@gmail.com , vikas.chouhan@unb.ca

*Corresponding Author: erarpita@gmail.com

 

Text Box: Abstract Wireless Sensor Networks (WSNs) are crucial in several applications, highlighting the need of effective clustering and fault detection systems. This paper introduces a novel approach that uses Reinforcement Learning (RL) and Particle Swarm Optimization (PSO) to optimize cluster head selection and enhance fault detection capabilities within WSNs. The proposed hybrid algorithm operates in two phases, combining the explorative capabilities of RL with the optimization process of PSO to select cluster heads based on residual energy and connectivity considerations. By continuously monitoring the network's residual energy state and the number of active nodes, the proposed method ensures prolonged network lifetime and improved overall performance. Our experimental results demonstrate the superior performance of the hybrid RL-PSO approach compared to traditional clustering algorithms, showcasing significant improvements in optimizer accuracy, residual energy preservation, and fault detection efficiency. Received: August 17, 2023   Revised: November 11, 2023  Accepted: January 11, 2024

 

 

Keywords: Wireless Sensor Networks (WSNs); Clustering; Reinforcement Learning (RL); Particle Swarm Optimization (PSO) and Fault Detection