IoT based Appliances Identification Techniques with Fog Computing for e-Health

Abstract

To improve the living standard of urban communities and to render the healthcare services sustainable and efficient, e-health system is experiencing a paradigm shift. Patients with cognitive discrepancies can be monitored and observed through the analyses of power consumption of home appliances. This paper surveys recent trends in home-based e-health services using metered energy consumption data. It also analyses and summarizes the constant impedance, constant current and constant power (ZIP) approaches for load modelling. The analysis briefly recaptures both non-intrusive and intrusive techniques. The work reports an architecture using IoT technologies for the design of a smart-meter, and fog-computing paradigm for raw processing of energy dataset. Finally, the paper describes the implementation platform based on GirdLAB-D simulation to construct accurate models of household appliances and test the machine-learning algorithm for the detection of abnormal behaviour.