EE-FGCH: An Energy-Efficient Fuzzy-Genetic Clustering Hierarchy for Wireless Sensor Networks

Wireless sensor networks (WSNs) face critical energy constraints due to limited battery life and processing capabilities, making energy optimization a core challenge. Prolonging network lifetime requires intelligent resource management at the node level. Dynamic clustering effectively reduces long-range transmissions to the base station, eliminates redundant data, and shortens routing paths, yielding significant energy savings while enhancing scalability for large-scale deployments. However, traditional clustering protocols suffer from sensitivity to cluster-head selection, load imbalance, and uneven node distribution, often leading to premature node failures and reduced longevity. This paper proposes EE-FGCH, a novel energy-efficient hierarchical clustering framework that integrates fuzzy logic for candidate pre-screening with a multi-objective genetic optimization algorithm for refinement. Simulation results demonstrate that EE-FGCH substantially outperforms the DCRRP protocol in energy consumption, end-to-end delay, Media access delay, Packet error rate, Packet loss rate, Signal-to-noise ratio, and throughput.