An Efficient Clustering Algorithm for Enhancing the Lifetime and Energy Efficiency of Wireless Sensor Networks

Wireless Sensor Networks (WSNs), as a crucial component of the Internet of Things (IoT), are widely used in environmental monitoring, industrial control, and security surveillance. However, WSNs still face challenges such as inaccurate node clustering, low energy efficiency, and shortened network lifespan in practical deployments, which significantly limit their large-scale application. To address these issues, this paper proposes an Adaptive Chaotic Ant Colony Optimization algorithm (AC-ACO), aiming to optimize the energy utilization and system lifespan of WSNs. AC-ACO combines the path-planning capability of Ant Colony Optimization (ACO) with the dynamic characteristics of chaotic mapping and introduces an adaptive mechanism to enhance the algorithm’s flexibility and adaptability. By dynamically adjusting the pheromone evaporation factor and heuristic weights, efficient node clustering is achieved. Additionally, a chaotic mapping initialization strategy is employed to enhance population diversity and avoid premature convergence. To validate the algorithm’s performance, this paper compares AC-ACO with clustering methods such as Low-Energy Adaptive Clustering Hierarchy (LEACH), ACO, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Simulation results demonstrate that AC-ACO outperforms the compared algorithms in key metrics such as energy consumption optimization, network lifetime extension, and communication delay reduction, providing an efficient solution for improving energy efficiency and ensuring long-term stable operation of wireless sensor networks.