GWO-PEGASIS: A Swarm-Intelligence-Based Protocol for Energy-Efficient Shortest-Path Construction in WSNs

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Chain-based routing protocols such as PEGASIS reduce redundant transmissions in Wireless Sensor Networks (WSNs), yet their static link structures and greedy neighbor selection often result in long communication links, uneven energy depletion, and premature network failure. Achieving both globally optimized chain construction and energy-balanced routing under diverse network deployments therefore remains a significant research challenge. To address these limitations, this paper proposes GWO-PEGASIS, a scalable two-stage optimization framework that integrates K-means clustering for spatial partitioning with the Grey Wolf Optimizer (GWO) to construct globally optimized intra- and inter-cluster chains under connectivity constraints. In the first stage, K-means forms spatially compact clusters based on node density, initial energy, and spatial distribution. In the second stage, GWO enables adaptive chainhead selection, balanced multihop forwarding, and improved spatial load distribution. Extensive simulations validate the effectiveness of the proposed method: GWO-PEGASIS shortens total chain length by 10.46% relative to PEGASIS, more than doubles the network lifetime, and further improves lifetime by 11.2% and 14.6% over EB-PEGASIS-SCL and MC-CRITIC-KM, respectively. It also achieves the lowest per-round energy consumption, the most balanced residual-energy distribution, and the highest throughput among all evaluated protocols. These results demonstrate that GWO-PEGASIS offers a robust, energy-efficient, and scalable routing solution for WSNs, highlighting the strong potential of swarm-intelligence-driven optimization for addressing complex topology design challenges in resource-constrained environments.