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Inference system of body sensors for health and internet of things networks

Kang, James, Luan, Tom and Larkin, Henry 2016, Inference system of body sensors for health and internet of things networks, in MoMM 2016: Proceedings of the 14th International Conference on Advances in Mobile Computing and Multimedia, Association for Computing Machinery, New York, N.Y., pp. 92-96, doi: 10.1145/3007120.3007145.

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Title Inference system of body sensors for health and internet of things networks
Author(s) Kang, James
Luan, Tom
Larkin, Henry
Conference name Advances in Mobile Computing and Multimedia. International Conference (14th : 2016 : Singapore)
Conference location Singapore
Conference dates 28-30 Nov. 2016
Title of proceedings MoMM 2016: Proceedings of the 14th International Conference on Advances in Mobile Computing and Multimedia
Editor(s) Abdulrazak, B.
Pardede, E.
Steinbauer, M.
Khalil, I.
Publication date 2016
Start page 92
End page 96
Total pages 5
Publisher Association for Computing Machinery
Place of publication New York, N.Y.
Keyword(s) Body Sensors
WBAN
IoT
mHealth
Personal Sensor Device
Body Sensor Network
Summary Wearable devices have become popular and innovative and areconverging with technologies such as big data, Cloud and Internetof Things (IoT). Traditional physiological sensors in fitnesstracking and mHealth provide health data periodically or arecaptured manually when required. In future, physicians as well asIoT devices will benefit from this data to provide their services.These situations can cause rapid battery consumption, consumesignificant bandwidth, and raise privacy issues. There have beenmany attempts to extend battery life and improve communicationmethodologies; however, they have not been able to solve theresource constraints arising from physical hardware limits, such asthe size of sensors. As an alternative, this paper presents a novelapproach and solution to controlling body sensors to reduce bothunnecessary data transmission and battery consumption. This canbe done by implementing an inference system on sensors usingsensed data to transfer it efficiently to other networks withoutburdening the workload from IoT onto sensor devices. In this paper,we experimented with reducing the bandwidth requirements forheart-rate sensors. Our results show savings in resource usage ofbetween 66% and 99%. Such savings have the potential of makingalways-on mHealth devices a practical reality.
ISBN 9781450348065
Language eng
DOI 10.1145/3007120.3007145
Field of Research 080702 Health Informatics
080301 Bioinformatics Software
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2016, ACM
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090908

Document type: Conference Paper
Collection: School of Information Technology
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