SFoG-RPI: A Secured QoS Aware and Load Balancing Framework for FoG Computing in Healthcare Paradigm

Abstract

Diabetes, characterized by persistently high blood glucose levels, has been identified as a hazardous health condition, potentially leading to severe complications such as heart attacks, strokes, and heart failure. This study introduces a fog-based remote health monitoring system designed to mitigate the devastating impacts of diabetes and hypoglycemia. This system persistently monitors health parameters including glucose levels, carbohydrate intake, physical activities, heart rate, and blood pressure. It additionally supports advanced services such as feature extraction, distributed local storage, and enhanced security. The traditional cloud-based architecture, while effective, often results in significant latency due to the processing of vast amounts of data. By bringing computing servers closer to users, Fog computing addresses this issue, reducing latency, and increasing security, resource accessibility, and on-demand scaling. In this context, the proposed system aims to minimize latency and network usage while addressing critical issues such as security, access control, and privacy. It employs lossy data compression at the gateway level to decrease network bandwidth and enhance efficiency. Furthermore, the system introduces a novel Load Balancing mechanism to distribute the load among fog nodes evenly. It utilizes lightweight cryptographic algorithms, efficient key exchange protocols, and digital signatures to ensure confidentiality, authentication, and user privacy. The performance of the proposed framework was evaluated in terms of average processing time, energy consumption management, computational resource distribution, latency, and network usage. When compared with other systems, the proposed framework demonstrated superior results, thus validating its effectiveness.