IoT-Based Remote Patient Monitoring Using Edge Computing

Abstract

Remote patient monitoring has become increasingly important for continuous healthcare delivery, especially for managing chronic diseases and reducing hospital visits. This paper presents an IoT-Based Remote Patient Monitoring system using Edge Computing to enable real-time health data analysis and efficient decision-making. The proposed framework integrates wearable sensors and IoT devices to collect physiological parameters such as heart rate, blood pressure, and body temperature. Edge computing is utilized to process data locally, reducing latency and minimizing dependence on cloud infrastructure. Machine learning algorithms are incorporated at the edge layer to detect anomalies and provide early warnings for potential health risks. The system ensures data privacy and security through encryption and secure communication protocols. Experimental observations indicate that the proposed approach improves response time, reduces network bandwidth usage, and enhances overall system reliability. Additionally, integration with prior research in healthcare analytics strengthens the robustness and scalability of the system. The study highlights the potential of combining IoT and edge computing technologies to enable efficient, real-time, and cost-effective remote patient monitoring solutions.