An Energy-Efficient Pathfinding Model for Wireless Sensor Networks in IoT Using Whale Optimization Algorithm

Abstract

The Internet of Things (IoT) offers the ability of device-to-device seamless connectivity, which enables real-time data collection and collaboration. Wireless Sensor Networks (WSNs), which are collections of geographically dispersed sensor nodes, are integral to IoT systems but suffer from low energy, storage, and wasteful data transmission, causing network instability, latency, and high energy consumption. To address these issues, the current research proposes a novel Pathfinding algorithm based on the Improved Whale Optimization Algorithm (IWOA) for WSNs. The aim of the current research is to enhance the network's performance by optimizing energy consumption, hop count, and data transmission efficiency. The proposed method utilizes intermediate sensors and optimizes the transmission paths step by step with the assistance of IWOA, thus performing efficient energy-saving data routing. The simulation outcomes indicate that the Whale Optimization Algorithm outperforms the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) approaches with 30% improvement in network lifetime, 10% higher number of active nodes, 15% higher successful packet deliveries, and 17% lower data transmission delay. These results illustrate the effectiveness of the introduced algorithm in maximizing WSN performance and hence are an important contribution to decentralized peer-to-peer and distributed systems.