Efficient clustering is essential for reducing energy consumption and extending the operational lifetime of Wireless Sensor Networks. Classical protocols such as LEACH, PEGASIS, HEED, and EEHC frequently exhibit unbalanced energy usage, resulting in early node failures and reduced communication reliability. This study introduces an Enhanced Grey Wolf Optimization-based Neural Network (EGWO-NN) designed to adaptively select cluster heads by continuously optimizing decision parameters according to real-time network conditions. The proposed method is evaluated against four benchmark protocols using statistical comparisons of node survivability, transmission energy, and communication performance. Results show that EGWO-NN sustains significantly more alive nodes per round, with strong statistical differences compared with LEACH, PEGASIS, HEED, and EEHC (t = 18.27, 9.94, 18.91, 18.93; p < 10−22). Transmission energy analysis similarly indicates significant improvements across all pairwise tests (|t| = 4.12–46.34; p < 10−4), supported by an overall ANOVA result (F = 14.74, p = 1.42×10−10). EGWO-NN also enhances data delivery, outperforming baseline protocols in both packets sent and Packet Delivery Ratio, with highly significant differences (t = 17.62–19.75 and 11.25–22.89). These findings demonstrate that EGWO-NN provides a robust and scalable approach for improving energy efficiency and communication reliability in WSNs.
Fauzan et al. (Sun,) studied this question.