Machine Learning-Based Energy-Aware Routing for Wireless Body Area Networks

Wireless Body Area Networks (WBANs) consist of small, low-power sensors placed on or inside the human body for real-time health monitoring. One of the primary challenges in WBANs is ensuring energy efficiency, given the limited power capacity of biosensor nodes. Recent advancements in the field, including sophisticated techniques such as clustering, Particle Swarm Optimization (PSO), reinforcement learning, and hybrid Machine Learning (ML) approaches, have demonstrated significant improvements over traditional routing methods. This paper investigates energy-aware routing in WBANs using ML models, specifically Decision Tree (DT), K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Linear Regression (LinReg). The objective of this study is to predict energy consumption per transmission and analyze the resulting impact on network lifetime. The study visualizes results through energy depletion curves and alive node count plots. The findings demonstrate that ML models can effectively predict energy consumption, enabling optimized packet transmission and extended network lifespan. Moreover, the analysis identifies the most efficient ML-based routing strategy for WBANs and demonstrates that ML approaches outperform existing methods in the literature.