FOG-ASSISTED REAL-TIME MONITORING OF SMART HEALTHCARE DEVICES

Abstract

Smart healthcare systems increasingly rely on continuous monitoring of physiological parameters from wearable and implantable devices. However, cloud-only architectures suffer from latency, bandwidth limitations, and privacy concerns, making them unsuitable for real-time medical applications. This paper introduces a fog-assisted monitoring framework that enables low-latency processing, early anomaly detection, and secure data transmission for healthcare IoT devices. Fog nodes perform local computation, adaptive filtering, and emergency alert generation, while the cloud executes long-term analytics and patient history modeling. Experimental simulation shows significant improvements in response time, network efficiency, and reliability. The proposed model supports scalable, robust, and real-time healthcare monitoring suitable for continuous patient care.