Green Routing Protocols for IoT-Based Software-Defined Wireless Sensor Networks: A Comparative Analysis

The rapid proliferation of Internet of Things (IoT) devices has led to the development of Software-Defined Wireless Sensor Networks (SDWSNs), which require efficient routing protocols to optimize energy consumption and network longevity. Traditional Wireless Sensor Networks (WSNs) suffer from inefficient routing and energy depletion, leading to reduced operational lifespans. To address these challenges, researchers have proposed bio-inspired and optimization-based green routing protocols, such as the Energy-Optimized Artificial Hummingbird Algorithm (EOAHA), Equilibrium Optimization-based Clustering (EO-C), and Multi-Constrained Green Routing Protocol (EQ-AHA). This paper presents a comparative analysis by focusing on their methodologies, network models, fitness functions, energy models, and performance metrics used by these three protocols. And the study discusses their effect on network lifetime, energy efficiency, and the reliability of data communication. The simulation results show that EQ-AHA outperforms EO-C and EOAHA by prolonging the first node death (FND) threshold for up to 1612 rounds and retaining higher residual energy in a multi-hop manner for energy conservation.The findings suggest that EQ-AHA is the most suitable protocol for large-scale IoT-SDWSNs, providing superior energy optimization and extended network lifetime.