A Hybrid Framework Integrating Deterministic Clustering, Neural Networks, and Energy-Aware Routing for Enhanced Efficiency and Longevity in Wireless Sensor Network

Wireless Sensor Networks (WSNs) have emerged as crucial tools for real-time environmental monitoring through distributed sensor nodes (SNs). However, the operational lifespan of WSNs is significantly constrained by the limited energy resources of SNs. Current energy efficiency strategies, such as clustering, multi-hop routing, and data aggregation, face challenges, including uneven energy depletion, high computational demands, and suboptimal cluster head (CH) selection. To address these limitations, this paper proposes a hybrid methodology that optimizes energy consumption (EC) while maintaining network performance. The proposed approach integrates the Low Energy Adaptive Clustering Hierarchy with Deterministic (LEACH-D) protocol using an Artificial Neural Network (ANN) and Bayesian Regularization Algorithm (BRA). LEACH-D improves upon conventional LEACH by ensuring more uniform energy usage across SNs, mitigating inefficiencies from random CH selection. The ANN further enhances CH selection and routing processes, effectively reducing data transmission overhead and idle listening. Simulation results reveal that the LEACH-D-ANN model significantly reduces EC and extends the network’s lifespan compared to existing protocols. This framework offers a promising solution to the energy efficiency challenges in WSNs, paving the way for more sustainable and reliable network deployments.