Software architecture for pervasive critical health monitoring system using fog computing

• DOI: 10.1186/s13677-022-00371-w
• Publication Date: 2022-12-05T14:41:37Z
• Authors: Abeera Ilyas; Mohammed Naif Ala...; Yasir Hamid; Saeed Mahfooz; Islam Zada; Neelam Gohar; Mohd Asif Shah
• Keywords: Computer engineering. Computer hardware; IoT; Artificial intelligence; IoT Security; Computer Networks and Communications; Activity Recognition in Pervasive Computing Environments; Fog Computing; 02 engineering and technology; Latency (audio); Real-time computing; Visual arts; TK7885-7895; Engineering; Architecture; FOS: Electrical engineering, electronic engineering, information engineering; 0202 electrical engineering, electronic engineering, information engineering; Cloud computing; Electrical and Electronic Engineering; Embedded system; Internet of Things and Edge Computing; Low Power Wide Area Network Technologies; Research; QA75.5-76.95; Computer science; Health Monitoring; Distributed computing; 3. Good health; Operating system; Electronic computers. Computer science; Computer Science; Physical Sciences; Telecommunications; Computer Vision and Pattern Recognition; FOS: Civil engineering; Software; Art; Embedding

AbstractBecause of the existence of Covid-19 and its variants, health monitoring systems have become mandatory, particularly for critical patients such as neonates. However, the massive volume of real-time data generated by monitoring devices necessitates the use of efficient methods and approaches to respond promptly. A fog-based architecture for IoT healthcare systems tends to provide better services, but it also produces some issues that must be addressed. We present a bidirectional approach to improving real-time data transmission for health monitors by minimizing network latency and usage in this paper. To that end, a simplified approach for large-scale IoT health monitoring systems is devised, which provides a solution for IoT device selection of optimal fog nodes to reduce both communication and processing delays. Additionally, an improved dynamic approach for load balancing and task assignment is also suggested. Embedding the best practices from the IoT, Fog, and Cloud planes, our aim in this work is to offer software architecture for IoT-based healthcare systems to fulfill non-functional needs. 4 + 1 views are used to illustrate the proposed architecture.