Adaptive forest fire optimization algorithm for enhanced energy efficiency and scalability in wireless sensor networks

Abstract

Energy-efficient routing is a fundamental challenge in Wireless Sensor Networks (WSNs) due to constrained node energy. Traditional optimization techniques such as Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) struggle to adapt to dynamic energy variations and network topology changes, leading to suboptimal energy utilization and premature node depletion. To address these limitations, this paper introduces the Forest Fire Optimization for Energy-Aware Routing (FFO-WSN), a novel routing algorithm inspired by fire propagation dynamics. The FFO-WSN model dynamically adjusts routing paths based on real-time energy levels, prioritizing high-energy nodes while avoiding energy-depleted ones, thereby enhancing network longevity and data transmission efficiency. Extensive simulations demonstrate that FFO-WSN outperforms ACO, PSO, and other hybrid nature-inspired methods, achieving 21.8% lower energy consumption, a 26.2% increase in network lifetime, 98.1% packet delivery ratio, and 1.02 Mbps throughput while maintaining low end-to-end delay. These results confirm the scalability and resilience of FFO-WSN, making it a promising solution for IoT-based health monitoring and smart city applications.