Optimized fuzzy logic-based clustering protocol for enhanced lifetime in wireless sensor networks using particle swarm optimization

Reduction of energy consumption and extension of network lifetime are crucial in the evolving Wireless Sensor Network (WSN) applications due to the energy constraint of sensor nodes. These nodes, limited by their small battery capacity and deployed in inaccessible areas, become inactive once their energy is depleted. Clustering techniques, particularly fuzzy-logic-based ones, have been extensively researched due to their ability to balance energy consumption by restricting data forwarding to cluster heads (CHs). In fuzzy logic, the fuzzy rules are based on either human expert knowledge of the application or optimization by heuristic, metaheuristic, fuzzy logic, or hybrid algorithms. This paper proposes a Particle Swarm Optimization (PSO) optimized protocol that generates an optimized fuzzy rule base for CH selection. The motivation behind this optimization is that the use of manually set rules to determine outputs may lead to significantly less optimal choices of CHs as the network is scaled to larger sizes. The proposed protocol refines the manually set initial rules by using the PSO algorithm to determine the output values of the rules and to apply weights to them depending on the input variables' impact level - residual energy, centrality, and mobility – on the CH selection. A better choice of CHs translates to more efficient energy utilisation in the network and hence extended network lifetime, which shows the significance of this work. Simulations in MATLAB show that the proposed protocol significantly outperforms existing protocols, measured based on the network lifetime parameters: First Node Dead (FND), Half Node Dead (HND), and Last Node Dead (LND). Comparative analysis with the IEECP, LEACH-PRO, DPFCP, and EECHIGWO protocols shows 297.38%, 89.75%, 201.99%, and 15.17% LND improvement, respectively, corresponding to the network lifetime extension. The overall analysis shows that the model achieves a slower node depletion rate and a higher node participation time, demonstrating the protocol's superiority and potential in WSN deployment.