Efficient IoT-Driven Healthcare Systems Utilizing Fog Computing, AI Models, and Data Routing Algorithms for Real-Time Decision Making

Abstract

The goal of this study is to improve patient care and enable real-time decision-making by enhancing an advanced Internet of Things (IoT)-based healthcare system using fog computing, data routing algorithms, and Artificial Intelligence (AI) models. AI models for anomaly detection, fog computing for low-latency data processing, IoT devices for continuous patient data collecting, and improved data routing for smooth communication between cloud systems, fog nodes, and IoT devices are all integrated into the system. Incorporating mathematical models for data routing optimization, latency minimization, and AI training ensures system scalability, accuracy, and efficiency. By reducing latency, increasing throughput, and improving decision-making accuracy, the suggested solution successfully tackles issues in real-time healthcare monitoring. Healthcare service delivery has significantly improved when performance is evaluated using important parameters such as accuracy, dependability, scalability, throughput, energy usage, and latency. The system achieves an overall accuracy of 96.8%, according to the data, which is a significant  improvement above traditional methods. In addition to improving the effectiveness of real-time patient monitoring, this integrated strategy opens the door for context-aware, adaptive, and predictive healthcare solutions, which will ultimately improve patient outcomes and healthcare
infrastructure.