Adjusted Rand Index-Guided DPSO for Clustering and Data Routing in Wireless Sensor Networks

Establishing an energy-balanced data routing and clustering approach is among the most fundamental steps to extend the longevity of wireless sensor networks (WSNs). This study presents an intelligent and energy-aware framework for data routing and clustering in WSN employing an adjusted Rand index (ARI)-guided discrete particle swarm optimization algorithm: ARI-DPSO. This method uses Dijkstra's algorithm to establish energy-efficient data paths and uses the network lifetime as the ARI-DPSO's fitness function. The discrete particle swarm optimization searches for the globally optimal cluster configuration that extends the network's operational lifetime. The novelty of the ARI-DPSO lies in its capability to avoid premature convergence by using the ARI metric to quantify the similarity between the swarm's global best solution and the current particles. Based on this level of similarity, ARI-DPSO employs an ARI-derived mechanism to trigger a dynamic perturbation element to force the swarm of particles to explore new search areas. The simulation results show that the ARI-DPSO improves the dynamics and diversity of the swarm, thereby maximizing the stable operational lifetime of wireless sensor networks (first node death).