A Model-Driven Framework for Design and Analysis of Fog Based e-Health System

Abstract

The ehealth intervention is being used on a daily basis as the increase in the disease data such as diabetes, heart disease, lungs, neurological disorders, malignancies, and others. As a result, the volume of patient data is consuming a lot of storage space. However, advances in the field of fog computing are assisting in the storage of the massive amounts of data generated on a daily basis. Because the eHealth system is mission-critical, it is expected to be flawless. There is no room for error in the system. Furthermore, an early design and analysis of an ehealth system is required. State-of-the-art fog based ehealth techniques, on the other hand, are hard to come across in the literature. In the area of fog computing, there exist some tools (e.g., iFogSim), which provide simulation and analysis, however, it does not support modeling, which limits user’s ability to design and analyze the system at early stages of SDLC. No code generation facility from the high-level model is available in fog computing tools. As a result, Model-Driven Fog based eHealth System Framework, an open-source framework for the construction and analysis of fog based ehealth systems, is proposed in this paper (MFeHF). Unified Modelling Language Profile for Fog based eHealth system (UMLPFeH) is proposed in the proposed framework for modelling the requirements of health and fog, where the concepts of ehealth and fog are divided into four components that deal with ehealth data and data processing through fog computing. The MFeHF has created an open-source transformation engine that uses the Acceleo tool to convert UML models to JAVA language. Two case studies, (i) EEG tractor beam and (ii) smart glove, are used to validate the feasibility, cost-effectiveness, and scalability of the MFeHF for eHealth system analysis. The experimental results show that the suggested framework is a simple and reusable method for developing fog based ehealth systems.