An IoT-Enabled Secure Framework for Intelligent Remote Patient Monitoring and Preventive Healthcare

Abstract

The rapid expansion of Internet of Things (IoT) technologies has significantly transformed healthcare delivery by enabling continuous remote patient monitoring (RPM) and data-driven preventive care. However, there are significant issues with information safety; patient privacy, connectivity, and real-time forecasting that arise when linked medical devices are used widely. Through an integrated, multi-layered architecture, this study suggests a safe IoT framework for remote patient monitoring and predictive preventive healthcare. The suggested system uses ambient and wearable sensors to gather physiological data in real time, such as blood pressure, pulse, oxygen saturation, and sugar levels. To guarantee quick response and bandwidth optimization, an edge computing layer carries out preliminary data filtering, identifying anomalies, and latency-sensitive processing. Processed data is safely sent to a cloud system for long-term storage and sophisticated analytics. The framework incorporates multi-factor authentication, blockchain-based integrity authentication, and light encryption technologies to improve security and stop illegal access and data manipulation. Furthermore, privacy-preserving predictive modeling is made possible without disclosing private patient information through the use of federated learning approaches. In order to enable prompt medical intervention and lower hospital readmission rates, machine learning algorithms examine gathered health indicators to find early risk patterns linked to chronic diseases. When compared to traditional cloud-centric models, experimental evaluation shows better detection accuracy, reduced latency, and increased resistance to cyber threats. The suggested framework facilitates the shift to preventative, value-based, and intelligent medical care methods by aiding in the creation of safe, scalable, and patient-centered remote medical systems.