Integration Of Edge And Fog Computing In Iot-Based Healthcare Applications - A Review

Abstract

The use of the Internet of Things (IoT) in the healthcare industry has the potential to improve healthcare services and outcomes through the use of connected devices and technology. By collecting and analyzing a large amount of data from various sensors and devices, the combination of IoT and centralized cloud computing can enable healthcare professionals to remotely monitor patients in near real-time, accurately diagnose conditions, and provide more personalized and efficient treatment through the use of artificial intelligence. However, high latency, low storage, lack of geographical location awareness, network failure, and security and privacy issues remain challenges in the adoption of IoT and cloud computing in the healthcare industry. To address these weaknesses, there is increasing interest in using edge and fog computing, which brings cloud computing capabilities closer to IoT devices through the use of intermediary nodes or gateways to process and transmit data, rather than sending all data to a central cloud server. This can reduce latency, improve data security and privacy, and allow for more efficient use of resources. This paper provides a review of state-of-the-art of edge and fog computing, its integration with the IoT, and the benefits and challenges of implementing the fog model in healthcare applications. It also covers various edge and fog computing architectures and how they can be used to improve emerging IoT applications, including potential future research directions related to fog computing, and AI in edge/fog layer in the IoT-based healthcare applications.