Long-Range, Low-Power IoT for Adaptive Biomedical Monitoring: AI-Driven Analytics for Remote Patient Care

Abstract

The integration of Long-Range, Low-Power Internet of Things (IoT) technologies into biomedical monitoring has revolutionized remote healthcare by enabling real-time data collection with minimal energy consumption. This paper proposes an AI-driven, adaptive biomedical monitoring system leveraging LoRaWAN and AI-based analytics to enhance patient surveillance in remote and resource-constrained areas. The proposed system integrates wearable biosensors, ultra-low-power edge computing, and cloud-based AI algorithms to analyze vital parameters such as heart rate, oxygen levels, and blood glucose in real time. An adaptive cognitive sensor node is implemented to dynamically adjust sensing frequency based on patient conditions, thereby optimizing energy efficiency while maintaining high diagnostic accuracy. Advanced compressed sensing and predictive analytics minimize data transmission, reducing power consumption and extending device lifespan. The system is designed to work seamlessly with Unmanned Aerial Vehicles (UAVs) and LPWAN networks to facilitate data collection in unconnected remote regions. By combining AI-driven anomaly detection with blockchain-based data security, the proposed framework ensures reliable, privacy-preserving, and intelligent remote healthcare monitoring. Experimental evaluations demonstrate a significant reduction in energy consumption compared to traditional monitoring systems while improving diagnostic efficiency. This research paves the way for scalable, cost-effective, and robust IoMT (Internet of Medical Things) solutions for global healthcare accessibility.