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IoT based Appliances Identification Techniques with Fog Computing for e-Health
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electronics and Embedded systems, Integrated devices and circuits. Univ Turku, Turku, Finland..ORCID iD: 0000-0003-2357-1108
Qassim Univ, Unaizah Coll Engn, Buraydah, Saudi Arabia.;Univ Monastir, Monastir, Tunisia..
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electronics and Embedded systems. Univ Dar Es Salaam, Dar Es Salaam, Tanzania..ORCID iD: 0000-0002-7734-7817
KTH, School of Electrical Engineering and Computer Science (EECS), Electrical Engineering, Electronics and Embedded systems. Univ Dar Es Salaam, Dar Es Salaam, Tanzania..
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2019 (English)In: IST-Africa 2019 Conference Proceedings / [ed] Paul Cunningham, Miriam Cunningham, IEEE , 2019Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
IEEE , 2019.
Keywords [en]
e-health., home management system, Internet of Things (IoT), fogcomputing, non-intrusive load monitoring and identification (NILM), smart-meter
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-263396DOI: 10.23919/ISTAFRICA.2019.8764818ISI: 000490550800003Scopus ID: 2-s2.0-85069928430ISBN: 978-1-905824-62-5 (print)OAI: oai:DiVA.org:kth-263396DiVA, id: diva2:1369979
Conference
IST-Africa Week Conference (IST-Africa), Nairobi, KENYA, MAY 08-10, 2019
Note

QC 20191113

Available from: 2019-11-13 Created: 2019-11-13 Last updated: 2019-11-13Bibliographically approved

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Kelati, AmlesetKondoro, AronRwegasira, DianaTenhunen, Hannu

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