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.
