HF-Snake-WERF: A Hybrid HHO–Firefly Clustering and Snake Optimizer-Based Energy-Efficient Routing Framework for Dynamic Wireless Sensor Networks

Abstract Wireless sensor networks (WSNs) face severe energy, communication, and resource constraints, so clustering and routing are essential for reliable, stable operation. Current methods often optimise cluster head (CH) selection and routing independently and rely on simplified assumptions that do not capture resource allocation, dynamic topology, channel impairments, and communication reliability. In this paper, we propose HF-Snake-WERF, a hybrid energy-efficient clustering and routing approach based on Harris Hawks Optimisation (HHO), Firefly Algorithm (FFA), and Snake Optimiser (SO) for dynamic WSNs. HHO performs global exploration for CH selection and routing, and Firefly Optimisation (FFO) performs local improvements in cluster quality based on residual energy, intra-cluster compactness, cluster separation, and cluster balance. Then, the Snake Optimiser finds energy-aware multi-hop routes based on transmission distance, residual energy and hop count. The framework also accounts for different node energy, communication range, queue capacity, processing delay, node mobility, path loss, shadowing, Rayleigh fading, noise, and SINR-based packet reception. Periodic reclustering adapts to different topologies and energy levels. In our experiments with traditional minimum-hop routing, we observe improvements in packet delivery ratio (PDR), throughput, and energy efficiency: average PDR rises from 0.603 to 0.869, throughput from 709.43 to 986.54 kbps, and normalised average energy consumption (NAEC) drops to 1.452 J/packet. Our approach therefore offers a coherent approach for reliable, energy-aware and adaptive WSN communication.