Adaptive Load-Balanced Clustering for Enhanced Energy Efficiency and Fault Tolerance in Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are critical in distributed monitoring systems, where optimal performance relies on efficient energy usage, balanced data handling, and fault resilience. Traditional clustering protocols, such as Energy-Aware Hybrid Clustering (EAHC), primarily focus on energy metrics but often neglect real-time load balancing and adaptive reconfiguration. This leads to node failures, uneven energy depletion, and increased latency. Clustering methods lacking dynamic adaptation to node load and failure conditions often face performance degradation due to hotspot formation, unbalanced load, and delayed reconfiguration. These issues negatively affect network lifetime, throughput, and data latency, particularly in large-scale and heterogeneous WSNs. The proposed Adaptive Load-Balanced Clustering (ALBC) model addresses these limitations by dynamically forming clusters based on real-time metrics such as node load, energy levels, data rates, and fault tolerance. A mathematical framework involving energy and data rate constraints, load variance minimization, and reconfiguration cost is developed. The cluster head (CH) selection process favors nodes with optimal energy-to-load ratios while ensuring connectivity and minimal latency. The model is validated using MATLAB simulations against four existing models: EAHC, LEACH, HEED, and EEHC. Simulation results show that ALBC significantly reduces energy consumption and latency while enhancing load balance and fault tolerance. It achieves up to 22% lower load variance, 18% higher throughput, and 30% fewer reconfigurations compared to EAHC. The model adapts seamlessly to node failures, ensuring uninterrupted data flow and prolonged network lifespan.