Optimizing Healthcare Through Fog Computing: Simulating and Performance Evaluation of Real-Time Health Monitoring Systems

Fog Computing (FC) is becoming increasingly vital in various sectors, including healthcare, smart transportation, and mobile communications. In the healthcare field, FC is crucial for enabling real-time health monitoring systems. This research examines the use of fog computing in various healthcare applications, highlighting its ability to analyze the health data in proximity to its source. This proximity reduces latency, execution costs, and network usage compared to traditional cloud-based methods. The proposed fog-based real-time healthcare monitoring architecture is composed of three main layers: Body Sensors layer, Fog Layer, and Cloud Layer. This configuration allows for the collection of health data from sensors, preprocessing and processing at the fog layer, and subsequent analysis and storage in the cloud layer. A simulation-based evaluation using iFogSim reveals significant improvements in latency, execution cost, and network efficiency compared to cloud-centric health monitoring systems. Experimental results show that using a fog device as an intermediate layer reduces latency, network usage, and execution costs by decreasing the need for frequent cloud access for health data transmission, processing, and status generation. This method improves real-time health monitoring and paves the way for scalable and efficient future healthcare solutions.