Hybrid Energy-Aware Framework for WSNs: Clustering, Routing, and Optimization Using PSO, Fuzzy Logic, and SOM

Abstract

The recent applications of Wireless Sensor Networks (WSNs) have attracted significant interests because of their broad application in industrial automation, healthcare monitoring, and environmental monitoring. Nevertheless, a significant issue in WSNs is how to maximize the energy use to increase the lifetime of the network without compromises in data delivery. The classical methods of clustering and routing, e.g., LEACH and its derivatives, tend to consider the selection of cluster heads in isolation and the optimization of routing in isolation, which results in uneven energy consumption, untimely nodes death, and inefficiency of the network as a whole. Furthermore, a lot of current metaheuristic techniques cannot dynamically adjust to the evolving network conditions, which leads to poor energy management. This paper presents an integrated approach that combines Particle Swarm Optimization (PSO), Fuzzy Logic, and Self-Organizing Maps (SOM) to achieve energy conservating approach using clustering and routing in WSNs. PSO is used for optimizing the clustering process by selecting optimal cluster heads, thus ensuring balanced energy distribution and reducing communication overhead. Fuzzy Logic is utilized to dynamically adjust the parameters of the network, such as node reliability and transmission power, based on real-time conditions, ensuring efficient resource utilization. Additionally, SOM is used to self-organize the network, facilitating the identification of optimal paths for data routing, which minimizes energy consumption and maximizes network lifetime. Moreover, the proposed SOM guided approach introduces a cross-layer interaction mechanism which helps to enhance the clustering stability and energy balance resulting in improved network lifetime. The incorporation of these techniques delivers a robust and adaptive solution to the energy challenges in WSNs. Simulation results demonstrate that the suggested approach significantly outperforms traditional clustering and routing strategies, offering enhanced energy efficiency, better network lifetime, and reduced packet loss.