Design and Evaluation of a Fog-Enhanced IoT Framework for Real-Time Health Monitoring in Rural Clinics

Abstract

The increasing burden on rural healthcare systems highlights the urgent need for affordable, real-time patient monitoring solutions. Traditional cloud-based Internet of Things (IoT) architectures face significant limitations in such contexts, including high latency, unreliable connectivity, and bandwidth constraints, which can compromise timely clinical decision-making. To address these challenges, this study proposes a fog-enhanced IoT framework tailored for rural clinics. The framework integrates patient-side IoT devices, local fog nodes, and a cloud backend, enabling real-time data processing closer to the source while maintaining long-term storage and advanced analytics in the cloud. The research adopts a design–evaluation approach. The proposed architecture is modelled and simulated using iFogSim, with performance measured against conventional cloud-only systems. Key evaluation metrics include latency, bandwidth utilization, energy consumption, and system reliability. Preliminary results indicate that the fog-enhanced system significantly reduces latency, optimizes bandwidth use, and improves service availability compared to cloud-only alternatives. A case scenario involving vital signs monitoring (heart rate, oxygen saturation, and ECG) demonstrates the framework’s effectiveness in supporting timely interventions for rural patients. The study contributes to the growing field of healthcare IoT by presenting a holistic and resource-efficient solution for underserved regions. Findings underscore the potential of fog computing to bridge healthcare disparities, while recommendations highlight future integration with artificial intelligence for predictive analytics and blockchain for secure medical data management.