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Inference of personal sensors in the internet of things

Kang, James Jin and Larkin, Henry 2016, Inference of personal sensors in the internet of things, International journal of information, communication technology and applications, vol. 2, no. 1, pp. 1-23, doi: 10.17972/ijicta20162125.

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Title Inference of personal sensors in the internet of things
Author(s) Kang, James JinORCID iD for Kang, James Jin orcid.org/0000-0002-0242-4187
Larkin, HenryORCID iD for Larkin, Henry orcid.org/0000-0001-5867-1542
Journal name International journal of information, communication technology and applications
Volume number 2
Issue number 1
Start page 1
End page 23
Total pages 23
Publisher Australasian Association for Information and Communication Technology
Place of publication Melbourne, Vic.
Publication date 2016
ISSN 2205-0930
Keyword(s) Inference
Data Mining
mHealth
Personal Sensor Devices
WBAN
Sensor Networks
IoT
Big Data
Cloud Computing
Summary Smartphone technology has become more popular and innovative over the last few years, and technology companies are now introducing wearable devices into the market. By emerging and converging with technologies such as Cloud, Internet of Things (IoT) and Virtualization, requirements to personal sensor devices are immense and essential to support existing networks, e.g. mobile health (mHealth) as well as IoT users. Traditional physiological and biological medical sensors in mHealth provide health data either periodically or on-demand. Both of these situations can cause rapid battery consumption, consume significant bandwidth, and raise privacy issues, because these sensors do not consider or understand sensor status when converged together. The aim of this research is to provide a novel approach and solution to managing and controlling personal sensors that can be used in various areas such as the health, military, aged care, IoT and sport. This paper presents an inference system to transfer health data collected by personal sensors efficiently and effectively to other networks in a secure and effective manner without burdening workload on sensor devices.
Language eng
DOI 10.17972/ijicta20162125
Field of Research 080702 Health Informatics
Socio Economic Objective 890103 Mobile Data Networks and Services
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Australasian Association for Information and Communication Technology
Persistent URL http://hdl.handle.net/10536/DRO/DU:30080976

Document type: Journal Article
Collection: School of Information Technology
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