Swarm Intelligence-Based Clustering For Self-Healing Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are increasingly deployed in mission-critical applications where network reliability and longevity are of paramount importance. However, node failures, energy depletion, and dynamic environmental conditions significantly degrade network performance. This paper proposes a swarm intelligence-based clustering framework for self-healing wireless sensor networks, enabling autonomous recovery and sustained network operation. The proposed approach leverages collective behaviors inspired by natural swarms to dynamically form, adapt, and optimize sensor clusters in response to topology changes and node failures. An adaptive clustering mechanism is developed using swarm optimization principles, where sensor nodes collaboratively elect cluster heads based on residual energy, link quality, and node centrality. Upon detection of node or link failures, a distributed self-healing protocol is triggered, allowing neighboring nodes to reconfigure cluster structures without centralized control. This ensures minimal communication overhead and rapid network restoration. Simulation results demonstrate that the proposed method achieves up to 38% improvement in network lifetime, 45% reduction in packet loss, and 30% lower energy consumption compared to conventional clustering protocols such as LEACH and HEED. Furthermore, the framework exhibits strong scalability and robustness under varying node densities and mobility conditions. The proposed swarm intelligence-based clustering approach significantly enhances fault tolerance and energy efficiency, making it suitable for long-term WSN deployments in smart agriculture, environmental monitoring, and disaster management applications.