EE-FGCH: An Energy-Efficient Fuzzy-Genetic Clustering Hierarchy for Wireless Sensor Networks
Year: 2025
Type: article
Abstract: 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.
Institution: Tabriz Islamic Arts University
Language: English
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7117125534
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