An Energy- Efficient Iot Architecture for Remote Healthcare Monitoring and Early Risk Detection

Deployment of IoT technologies in healthcare has facilitated continuous monitoring and timely diagnosis of diseases without the need for clinical settings. Yet, implementation of such deployment is hindered by some important obstacles associated with energy efficiency, communication delays, and large-scale risk analytics. This paper presents an energy-efficient IoT framework that combines edge-based processing of biosensors, adaptive communication, and light-weight detection of anomalies for remote patient monitoring. The architecture uses ESP32 microcontrollers with LoRa communication technology and dynamic transmission intervals as a trade-off between power usage and monitoring promptness. Hybrid edge-cloud analytics are employed for processing of physiological signals and timely risk detection. Evaluation of the architecture shows that the solution can operate in a sustainable manner with 5-second adaptive transmission intervals, achieving robust data delivery and saving much power at the same time. Comparison of the presented architecture with conventional IoT health frameworks proves its superiority in energy efficiency and latency metrics.