An Energy-Efficient Multiparameter Clustering and Routing Framework for WSNs Using Adaptive Starfish Optimization Algorithm and Spider-Tailed Horned Viper Optimization

Wireless sensor networks (WSNs) have become an essential component of environmental monitoring, military surveillance, industrial automation, and smart city applications. However, the limited energy capacity of sensor nodes and inefficient clustering and routing mechanisms significantly affect network lifetime, communication reliability, and quality of service (QoS). Existing approaches often rely on a limited number of parameters for cluster-head selection and routing decisions, resulting in unbalanced energy consumption, frequent cluster-head failures, increased communication overhead, and reduced network performance. To overcome these limitations, this work proposes an energy-efficient multiparameter clustering and routing framework that integrates the enhanced grid-based clustering algorithm (EGBCA), improved starfish optimization algorithm (ISFOA), and adaptive spider-tailed horned viper optimization (ASTHVO). EGBCA forms balanced clusters, while ISFOA selects optimal cluster heads using multiple parameters, including node energy, residual energy, distance to the base station, node degree, and intra-cluster distance. Subsequently, ASTHVO identifies reliable and energy-efficient routing paths to improve data transmission efficiency and network stability. Simulation results obtained in MATLAB demonstrate that the proposed framework achieves a network lifetime of 5500 rounds, energy consumption of 38 mJ, end-to-end delay (E2ED) of 3.5 ms, packet delivery ratio (PDR) of 98%, throughput of 0.9 Mbps, and bit error rate of 3%, outperforming existing methods across key QoS metrics. The proposed framework effectively improves energy efficiency, communication reliability, and network longevity, making it a promising solution for next-generation WSN deployments.