ARFOR: adaptive random forest and owl optimization for energy-efficient routing in WSN-IoT

Abstract

Wireless Sensor Networks (WSNs) in IoT environments face severe constraints that threaten both performance and lifetime. The primary challenge is the limited battery capacity of sensor nodes, which frequently causes premature network partitioning. Dynamic topologies driven by node mobility, hardware failures, or environmental interference further complicate reliable routing. When combined with fluctuating traffic demands in critical applications (healthcare, smart grids, environmental monitoring), these factors demand highly adaptive routing strategies. This paper proposes ARFOR, a hybrid framework that combines an enhanced Random Forest model with the Owl Optimization Algorithm (OOA). The Random Forest component, augmented by shared memory and an energy-aware attention mechanism, predicts topology changes and suggests initial routes. OOA then refines these paths using multi-modal network data while dynamically adjusting its parameters. Through continuous closed-loop adaptation, ARFOR aligns its behavior with real-time network conditions. Simulation results demonstrate that ARFOR increases residual energy by over 33%, reduces total energy consumption by 53%, and improves packet delivery ratio by more than 32% compared to recent state-of-the-art protocols. These gains make ARFOR particularly suitable for dynamic, energy-constrained WSN-IoT deployments in healthcare and smart grid systems.