Energy-Aware Adaptive Hybrid Clustering (Ea-Ahc) Model for Optimized Wireless Network Performance

Abstract

Efficient energy consumption remains a critical challenge in wireless network environments, where nodes operate under limited power resources. As wireless networks scale in size and complexity, traditional clustering protocols struggle to balance energy efficiency, connectivity, and fault tolerance. An effective solution requires a dynamic approach that intelligently adapts to the ever-changing network conditions. This paper introduces the Energy-Aware Adaptive Hybrid Clustering (EA-AHC) model, designed to optimize energy usage and enhance network lifetime through a dynamic and intelligent clustering strategy. Unlike static clustering schemes, EA-AHC adapts cluster configurations in real-time by factoring in node-specific attributes such as energy level, signal strength, fault status, and mobility. The approach integrates both intra-cluster and inter-cluster communication strategies, reducing communication overhead and ensuring robust connectivity. The proposed model is formulated mathematically, capturing multiple constraints like energy thresholds, signal strength, latency, cluster balance, and energy replenishment. A decision-making mechanism dynamically assigns nodes to clusters, ensuring optimal performance under variable operational scenarios. Simulations conducted in MATLAB using a network of 200 nodes demonstrate the superiority of EA-AHC compared to existing methods such as LEACH, HEED, EEHC, and TEEN. The proposed model shows improved energy conservation, reduced latency, better signal strength management, fewer cluster reconfigurations, and lower fault incidence. EA-AHC proves to be a scalable and adaptive solution for energy-constrained wireless network environments, enhancing both stability and network longevity.