FogDLearner: A Deep Learning-based Cardiac Health Diagnosis Framework using Fog Computing
Sundas Iftikhar, Muhammed Golec, Deepraj Chowdhury, Sukhpal Singh Gill, Steve Uhlig
Abstract
The application of the Internet of Things (IoT) and Artificial Intelligence (AI) in healthcare is an emerging domain. In Healthcare applications, relying on both IoT and AI requires paying attention to latency, responsiveness and management of data loads. Most of the healthcare applications are based on Cloud computing and use Cloud platforms such as Google Cloud and Microsoft Azure. With the increased adoption of IoT in various domains, the data generation rate and volume by IoT devices has tremendously increased, making the Cloud insufficient for latency sensitive healthcare applications. Fog computing, complementing the Cloud services, can be deployed close to the data source to better utilize distributed resources and meet the Quality of Service (QoS) requirements of healthcare application. In this paper, we propose a Fog-based cardiac health detection framework, called FogDLearner. FogDLearner utilizes distributed resources to diagnose cardiac health of a person without compromising QoS and accuracy. FogDLearner uses a deep learning based classifier to predict the cardiac health of the user. The performance of the proposed framework is evaluated on the PureEdgeSim simulator, in terms of resource utilization under overload and under-load scenarios, mobility support, and power consumption. The experimental results show the validity of proposed work for support of mobile applications.