Energy optimized artificial hummingbird algorithm for routing in IoT-based software-defined WSN

The Internet of Things (IoT) has become widely used in applications such as smart homes, industrial automation, and transportation due to its affordable hardware and fast internet connectivity. However, the increase in IoT-enabled gadgets, particularly those running on batteries or connected to other sources, is putting strain on the world's energy requirements. Therefore, this study focuses on a green routing solution for battery-powered IoT-enabled Software-defined Wireless Sensor Networks (IoT-SDWSN). Finding green solutions for IoT-based networks to address this energy challenge has become crucial. This study focuses on developing a green routing solution for battery-powered IoT-SDWSN. Energy efficiency in IoT-SDWSN is attained by the process of clustering nodes. The network is partitioned into small clusters, and a Control Node (CN) is set up by a Control Server (CS) to transmit the data packets sent by sensor nodes. Choosing a CN in these networks is a critical concern due to the substantial energy consumption involved in delivering data to the CS. This research focuses on the problem of energy-efficient cluster routing in IoT-based SD-WSN. It introduces the Energy-optimized Artificial Hummingbird Algorithm (EOAHA) as a green routing technique. EOAHA aims to extend the lifespan of IoT-based SD-WSNs by intelligently selecting (based on a new fitness function) CNs to distribute the network load and increase its overall longevity. To evaluate the performance of EOAHA, a comparative analysis is conducted against other state-of-the-art algorithms. The results demonstrate that EOAHA outperforms these algorithms by a minimum of 13.5% in terms of network longevity. The Energy-Optimized Artificial Hummingbird Algorithm (EOAHA) addresses the energy challenges caused by the increasing number of battery-powered IoT devices and suggests a green routing strategy for IoT-SDWSN. EOAHA forms node clusters in order to enhance energy efficiency by intelligently selecting control nodes (CNs). Its goal is to extend the lifespan of IoT-SDWSNs. Comparative analysis shows that EOAHA outperforms existing algorithms by at least 13.5% in terms of network longevity, highlighting its efficacy in mitigating energy challenges in IoT-based networks. image