Optimizing Clustering in Wireless Sensor Networks: A Synergistic Approach Using Reinforcement Learning (RL) and Particle Swarm Optimization (PSO)

Significant research efforts are currently devoted to wireless sensor networks due to its broad range of applications. WSNs face various constraints, encompassing challenges related to communication, clustering management and the finite battery life of nodes. Thus, Energy conservation in such networks is indispensable. Given a constant energy consumption rate during information sensing and reception, the highest energy consumption among sensor nodes occurs during data transmission. One of promising solution to reduce energy consumption is organizing WSN in clusters. Clustering in Wireless Sensor Networks (WSN) involves grouping sensor nodes into clusters to facilitate efficient data aggregation, communication, and management within the network. This organizational structure helps optimize energy consumption, enhance scalability, and prolong the overall lifespan of the WSN. However determining the optimal criteria for selecting cluster heads is challenging, as it involves balancing energy efficiency, network connectivity, and load distribution. In this paper, a dual-phase approach is proposed, firstly Reinforcement learning (RL) approach has been applied to clustering in WSNs which enables nodes to autonomously adapt their clustering strategies, leading to more efficient and adaptive network configurations. Further Particle Swarm Optimization (PSO) can be utilized for cluster head selection in Wireless Sensor Networks (WSNs) to optimize the formation of clusters. The consideration of both local and global perspectives in the proposed approach results in a more balanced and efficient clustering solution. The outcomes of our experiments demonstrate the enhanced performance of the integrated approach as compared to traditional clustering algorithms. Results show considerable improvement in terms of reduced energy consumption, accuracy and efficiency in fault detection specifically tailored for Wireless Sensor Networks (WSNs). In addition the proposed algorithm show enhanced residual energy of the nodes compared to previous methods used.