A Fitness Function-Based Approach For Wireless Sensor Network Clustering with Lungs Performance Optimizer

To gather and analyze physical data from the environment, Wireless Sensor Networks (WSNs) are created by a large number of spatially dispersed nodes on the wireless network. Problems with cluster head transmission line construction, redundancy of working nodes, and random selection of cluster heads all had an effect on the network and its energy usage. Because of this energy limitation, both the network longevity and energy-efficient routing are affected. Research on methods to reduce energy usage and increase the network's lifespan is, hence, focused on these two issues. Network performance is affected by energy consumption, communication overhead, and data transmission reliability, all of which are influenced by the fundamental process of cluster head (CH) selection. Using the Lungs Performance Optimizer (LPO), a bio-inspired optimization algorithm developed to improve the efficiency of CH selection and extend the lifetime of the network, to present an enhanced method for CH selection in this study. Our approach utilizes a fitness function incorporating key performance metrics: Average Intra-cluster Distance (AID), Average Sink Distance (ASD), Residual Energy (RE), and Cluster Head Balancing Factor (CHBF). The LPO algorithm ensures that CHs are optimally placed to minimize intra-cluster communication costs, maintain optimal distances from the sink node, and evenly distribute energy consumption among sensor nodes. Experimental evaluations demonstrate that the LPO-based CH selection method significantly outperforms conventional clustering techniques in terms of energy efficiency, network lifetime, and CH load balancing.