Reducing Latency and Enhancing Data Security in Healthcare IoT Systems Using Fog Computing

Abstract

This study presents an innovative fog computing framework aimed at optimizing biomedical data processing within healthcare information systems. The proposed architecture addresses critical latency and bandwidth limitations of traditional cloud-based systems, particularly for real-time and security-sensitive applications in healthcare. By processing data closer to IoT sources, our fog-based model reduces latency and optimizes network bandwidth, enabling timely responses and improved data security. Experimental results demonstrate significant improvements, including a 50% reduction in latency and a 20% increase in bandwidth efficiency, along with enhanced predictive accuracy in AI-driven health diagnostics. These findings suggest that fog computing offers a viable solution for healthcare applications requiring real-time data processing and high security.