Predictive data mining for converged internet of things: a mobile health perspective

Kang, James Jin, Adibi, Sasan, Larkin, Henry and Luan, Tom 2016, Predictive data mining for converged internet of things: a mobile health perspective, in ITNAC 2015 : Proceedings of the 25th International Telecommunication Networks and Applications Conference, IEEE, Piscataway, N.J., pp. 5-10, doi: 10.1109/ATNAC.2015.7366781.

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Title Predictive data mining for converged internet of things: a mobile health perspective
Author(s) Kang, James JinORCID iD for Kang, James Jin orcid.org/0000-0002-0242-4187
Adibi, Sasan
Larkin, HenryORCID iD for Larkin, Henry orcid.org/0000-0001-5867-1542
Luan, Tom
Conference name International Telecommunication Networks and Applications. Conference (25th : 2015 : Sydney, New South Wales)
Conference location Sydney, New South Wales
Conference dates 18-20 Nov. 2015
Title of proceedings ITNAC 2015 : Proceedings of the 25th International Telecommunication Networks and Applications Conference
Publication date 2016
Start page 5
End page 10
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) mHealth,
IoT
mIoT
Cloud
big data
Inference
Life expectancy
Health status
data mining
Summary Mobile Health (mHealth) is now emerging with Internet of Things (IoT), Cloud and big data along with the prevalence of smart wearable devices and sensors. There is also the emergence of smart environments such as smart homes, cars, highways, cities, factories and grids. Presently, it is difficult to quickly forecast or prevent urgent health situations in real-time as health data are analyzed offline by a physician. Sensors are expected to be overloaded by demands of providing health data from IoT networks and smart environments. This paper proposes to resolve the problems by introducing an inference system so that life-threatening situations can be prevented in advance based on a short and long term health status prediction. This prediction is inferred from personal health information that is built by big data in Cloud. The inference system can also resolve the problem of data overload in sensor nodes by reducing data volume and frequency to reduce workload in sensor nodes. This paper presents a novel idea of tracking down and predicting a personal health status as well as intelligent functionality of inference in sensor nodes to interface IoT networks
ISBN 9781467393485
Language eng
DOI 10.1109/ATNAC.2015.7366781
Field of Research 080702 Health Informatics
Socio Economic Objective 890103 Mobile Data Networks and Services
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30080977

Document type: Conference Paper
Collection: School of Information Technology
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