EELTM: An Energy Efficient LifeTime Maximization Approach for WSN by PSO and Fuzzy-Based Unequal Clustering

The Wireless Sensor Network (WSN) personifies vital and active functions in multi-disciplinary research sectors, as it can deploy in harsh and antagonistic atmospheres where the deployment of a wired system is not possible. However, designing an energy efficient and durable WSN is still a key challenge. Though the contribution of the clustering mechanism attempts to augment the network LifeTime, the energy consumption in the Cluster Heads (CHs) is rapidly high. This led to the frequent change of CHs and minimized the network’s lifetime. To diminish these issues, we propose an Energy Efficient LifeTime Maximization (EELTM) approach which utilizes the intelligent techniques Particle Swarm Optimization (PSO) and Fuzzy Inference System (FIS). Further, we propose an optimal CH–CR selection algorithm in our approach which exploits the fitness values calculated by the PSO technique to determine two optimal nodes in each cluster to act as CH and Cluster Router (CR). The selected CH exclusively gathers the information from its cluster members, whereas the CR is liable for receiving the gathered information from its CH and transferring it to the BS. Thus, the overhead of CH is reduced. Another intelligent technique is that FIS figures out the radius for each CH, and thereby it partitions the network into unequal clusters. The performance of our proposed EELTM approach is analyzed, and evaluations are elaborated with well-known existing clustering algorithms. To assess the proficiency of EELTM and to evaluate the endurance of the network, efficiency parameters such as total-remaining-energy, first-node-expires and fifty-percent-expires are exploited. The experimental outcomes justify that the EELTM approach surpasses the existing mechanisms by 14%.