Energy-Efficient Clustering and Routing Algorithm Using Modified Squirrel Search Optimization and Improved Remora Optimization for WSN

The development of WSNs has received significant interest in Smart Computing due of its many usefulness. Small, battery-powered, self-configured sensor nodes form WSN. Limited energy and materials limit sensors. Unbalanced energy nodes consume electricity and reduce the lifespan of networks. One of the most difficult problems in WSN routing design is still achieving energy efficiency. Methods for reducing node energy consumption by using clustering techniques have provided the best results. But without taking into account energy factors, selecting a low-energy node as the cluster head, node density, and scalability, the current clustering technique is unable to balance the energy of the nodes. Current schemes like Ant-Colony-Optimization (ACO), Honey Bee Optimization (HBO), and Genetic Algorithm (GA) are bound to particular search regions and only allow for global exploration options. In this paper, we propose an IROA- and MSSO-Cluster-based routing scheme for WSNs that uses less energy. The Modified Squirrel Search Optimization (MSSO) is used for cluster head selection in order to derive the multi-function derivation, and the Improved Remora Optimization Algorithm (IROA) is employed for routing in order to increase the stability of data delivery. MSSO-IROA (Cluster-based Routing) saves energy and extends network life. By comparing our proposed technique to the existing clustering and routing methods, we are able to increase Quality of Service (QoS) parameters such as Energy Consumption (10 mJ), packet delivery ratio(98.4%), throughput(0.99 Mbps), network lifetime(6100 rounds), and end-to-end delay(1.9 ms) using our proposed method.