Design of a Secure Edge-AI IoT Framework for Real-Time Healthcare Monitoring and Predictive Analytics

Abstract

Wearable sensors and IoT supporting healthcare devices are becoming more common for continually monitoring patients’ health. Such devices provide large amounts of data on body saturation with oxygen, heart rate, glucose level, blood pressure, etc. However, sending all this data straight to the cloud servers poses challenges related to latency and bandwidth and privacy risks. Even small delays in data processing can affect timely medical judgments in crucial healthcare circumstances. An integrated computing approach is needed to solve these issues. By processing data locally, the application of edge and fog computing technologies, minimizing the reliance on centralized cloud resources. However, sophisticated machine learning models are required to analyze real-time health information and identify signs of medical conditions. A secure Edge-AI-based IoT framework is suggested which enables healthcare monitoring in real-time and predicting the future health situations. Deep learning models are deployed at the edge nodes to detect medical anomalies and foretell health issues. Lightweight encryption technologies ensure reliable transfer of sensitive information, while blockchain technology is employed to create reliable logs of data access. The system actively allocates tasks to edge and cloud locations in terms of performance and efficiency. Tests show less latency, better predictive ability, and improved data protection compared to older, cloud-only methods of facilitating healthcare. The system makes possible the large-scale implementation of the technology in hospitals, remote monitoring facilities, and nursing homes, facilitating effective healthcare delivery.