Differential Evolution Optimized Fuzzy Logic Controller and D-Star Algorithm for Clustering Routing in WSNs

Abstract

Wireless sensor networks (WSNs), with advantages, such as easy deployment and efficient data collection, constitute a critical component of the Internet of Things. However, WSNs face significant challenges in energy efficiency and prolonging network lifetime. To mitigate these identified limitations, the present work introduces a differential evolution optimized fuzzy logic controller and D-star (DEFLCD) algorithm for clustering routing in WSNs. First, the differential evolution (DE) algorithm is enhanced by integrating a population initialization method based on the SPM chaotic map, along with adaptive scaling factors, crossover probabilities, and an elite individual selection strategy, thereby improving the algorithm’s exploitation capability. Second, the optimized DE algorithm is employed to refine the output membership functions of the fuzzy logic controller (FLC). An innovative fitness metric is formulated to quantify the optimized FLC’s efficacy in improving cluster performance, thereby enhancing operational adaptability and robustness in dynamic networking environments. In the packet forwarding stage, the D-star methodology dynamically classifies congested nodes as routing barriers and establishes power-efficient multihop links between cluster heads (CHs) and the base station, achieving balanced energy utilization and improved scalability across large-scale network infrastructures. The simulation outcomes show that DEFLCD surpasses the existing algorithms in various network performance assessment metrics, offering an energy-efficient routing solution for large-scale monitoring applications.