Energy Efficient Internet of Things Based Routing Algorithm

This paper provides a novel hybrid data routing algorithm that leverages metaheuristic techniques to enhance energy efficiency in Wireless Sensor Network (WSN) applications within the Internet of Things (IoT) framework. As high-speed networks continue to develop, the need for efficient IoT-enabled systems and services becomes increasingly crucial. This paper addresses these needs by providing an innovative solution that optimises data routing, thereby extending the operational life of WSNs and improving overall network performance. A dual comparative energy-efficient routing protocol for IoT applications, utilising the Lyrebird Optimization Algorithm (LOA) and Walrus Optimization Algorithm (WaOA) have been considered. We have proposed the comparative algorithm where Self adaptive LOA, and Self adaptive WaOA. Both have been separately applied to IoT-based routing, to overcome energy efficiency problems. Initially, these two proposed algorithms are individually analysed to choose a suitable algorithm for clustering and routing. The findings of this study demonstrate that the SaWOA algorithm is suitable for routing, while the SaLoA method is effective for clustering. By combining the strengths of these two algorithms, the performance of Cluster Head (CH) selection and routing in IoT networks has been improved. We have also compared and analysed the hybridization of the SaLoA and SaWOA algorithms, known as the SaLWOA algorithm, to achieve optimal performance. The findings show that SaLWOA provides up to a 25% improvement in network lifetime, 15-20% reduction in energy consumption, and 10-15% improvement in packet delivery ratio. Additionally, SaLWOA improves throughput by up to 63%, reduces latency by up to 45%, and maintains up to 32% more alive nodes compared to other well-known algorithms.