Elite-Opposition based Learning Strategy with Tuna Optimization Algorithm for Energy Efficient Clustering and Routing in IoT-WSN

Internet of Things (IoT) is a network of interconnected devices that communicate reliably with one another through the Internet.Wireless Sensor Networks (WSNs) play a significant role in IoTs by generating seamless data that greatly influences network's lifetime.However, energy efficiency is a challenge due to the limited battery life of nodes, which restricts their capacity for continuous sensing and communication.This research proposes an Elite-Opposition-based Learning Strategy with Tuna Optimization Algorithm (EOL-TOA) for energy-efficient clustering and routing in IoT-WSN.The EOL approach is incorporated into the traditional TOA to improve population quality and search effectiveness, which enhances global optimization capabilities and addresses the issue of local optima.Residual energy, node degree, and delay are the fitness parameters considered for Cluster Head (CH) selection.In EOL-TOA, the routing process is assessed based on energy and distance as fitness parameters.The EOL-TOA identifies and eliminates malicious nodes using a fitness function to ensure reliable data transmission.When compared to existing methods like Energy-Aware Clustering and Multihop Routing Protocol with Mobile Sink (EACMRP-MS), the proposed EOL-TOA has a lower energy consumption demand of 14.74 mJ for 100 nodes, thereby outperforming the existing approaches.