Integration of IoT and Cloud Computing in Smart Diabetes Management Systems: A Multidisciplinary Framework

Abstract

The convergence of the Internet of Things (IoT) and Cloud Computing is transforming healthcare delivery through the creation of intelligent, data-driven diabetes management systems. This study proposes a multidisciplinary framework integrating IoT-enabled biosensors, wearable glucose monitors, and cloud-based analytics to enhance real-time monitoring, prediction, and control of diabetes. By leveraging continuous glucose data, patient lifestyle parameters, and AI-driven cloud algorithms, the framework enables automated insulin adjustment, early risk detection, and remote physician supervision. The system’s design emphasizes interoperability, low-latency data exchange, and end-to-end security to ensure reliability and patient confidentiality. A hybrid cloud architecture combining edge computing for immediate responses with cloud storage for longitudinal data was evaluated for scalability and accuracy across multiple user profiles. The integration was tested using simulated patient datasets and IoT device networks, revealing a 23% improvement in glucose-level prediction accuracy and a 31% reduction in response latency. The proposed model demonstrates that merging IoT sensing capabilities with cloud intelligence can establish a seamless, patient-centric ecosystem, offering continuous, personalized care while reducing healthcare costs and burden on clinical resources.