Energy-Efficient and Fault-Tolerant Wireless Body Area Network Framework Leveraging Machine Learning Techniques

Wireless Body Area Networks (WBANs) enable continuous, real-time health monitoring via wearable and implantable sensors. Yet deploying them at scale raises challenges in energy efficiency, fault tolerance, security, and data throughput. We propose FlexiFault-Energy Optimized Routing (FlexiFEOR), a hybrid algorithm that merges Destination-Sequenced Distance-Vector (DSDV) routing with advanced node-connectivity and fault-management techniques, all underpinned by LSTM-based failure prediction. FlexiFEOR continuously analyzes link reliability and proactively reroutes traffic to avoid disruptions. In a Python-based simulation of a 20-node network, FlexiFEOR reduced end-to-end latency by 30%, increased packet throughput by 25%, and improved energy efficiency by 40% compared to DSDV and NCFM alone. Under fault conditions, it maintained seamless data delivery with rapid failover, while real-time AES encryption secured patient data in transit. These results demonstrate FlexiFEOR’s ability to deliver reliable, efficient, and secure data delivery in large-scale WBAN deployments. By addressing key scalability and resilience barriers, FlexiFEOR paves the way for next-generation smart-healthcare networks and improved patient outcomes.