A Nature-Inspired Multi-Objective Green Routing Protocol for Iot-Enabled SDWSNs

A smart city leverages technology and data to enhance the quality of life for its residents, improve urban services, and optimize resource management. The rapid rise in Internet of Things (IoT) devices has led to a significant surge in energy requirements, making energy optimization critical to mitigate this growing global demand. Clustering is a widely adopted technique to achieve energy optimization in IoT-enabled Software-Defined Wireless Sensor Networks (SDWSNs). In clustering, the network is divided into small groups, and a Cluster Head (CH) is chosen by a Control Station (CS) to forward data packets from sensing nodes. The role of CH is power-consuming as it aggregates data from its cluster and forwards it to CS; this may lead to hot-spot problems. Therefore, it is very important to select CH wisely. Hence, this article proposes an EO-C algorithm to address multiple objectives like hot-spot problems, network life, energy optimization, and reliability. EO-C aims to enhance energy efficiency in IoT-enabled SDWSNs by dynamically optimizing the selection process of CH using a novel fitness function based on residual energy, energy balance ratio, and alive node count. The simulation findings demonstrated that EO-C surpasses other SOA algorithms with an improvement in network lifespan ranging from 15.86% to 372.6%, showcasing its effectiveness across various scenarios. Additionally, EO-C exhibits robust scalability, effectively handling diverse node densities and deployment areas, making it a promising solution for sustainable IoT networks.