Deployment of an agent-based SANET architecture for healthcare services

Chiu, C; Chaczko, Z

Deployment of an agent-based SANET architecture for healthcare services

Chiu, C

Chaczko, Z

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Publication Type:: Journal Article
Citation:: International Journal of Electronics and Telecommunications, 2011, 57 (3), pp. 309 - 315
Issue Date:: 2011-09-01

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Field	Value	Language
dc.contributor.author	Chiu, C https://orcid.org/0000-0003-2045-5961	en_US
dc.contributor.author	Chaczko, Z https://orcid.org/0000-0002-2816-7510	en_US
dc.date.issued	2011-09-01	en_US
dc.identifier.citation	International Journal of Electronics and Telecommunications, 2011, 57 (3), pp. 309 - 315	en_US
dc.identifier.issn	2081-8491	en_US
dc.identifier.uri	http://hdl.handle.net/10453/18286
dc.description.abstract	This paper describes the adaptation of a computational technique utilizing Extended Kohonen Maps (EKMs) and Rao-Blackwell-Kolmogorov (R-B) Filtering mechanisms for the administration of Sensor-Actuator networks (SANETs). Inspired by the BDI (Belief-Desire-Intention) Agent model from Rao and Georgeff, EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize, while the Rao-Blackwell filtering mechanism reduces the external noise and interference in the problem set introduced through the self-organization process. Initial results demonstrate that a combinatorial approach to optimization with EKMs and Rao-Blackwell filtering provides an improvement in event trajectory approximation in comparison to standalone cooperative EKM processes to allow responsive event detection and optimization in patient healthcare.	en_US
dc.relation.ispartof	International Journal of Electronics and Telecommunications	en_US
dc.relation.isbasedon	10.2478/v10177-011-0041-8	en_US
dc.title	Deployment of an agent-based SANET architecture for healthcare services	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	57	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	1005 Communications Technologies	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building
pubs.organisational-group	/University of Technology Sydney/Faculty of Design, Architecture and Building/School of Architecture
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - INEXT - Innovation in IT Services and Applications
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	57	en_US

Abstract:

This paper describes the adaptation of a computational technique utilizing Extended Kohonen Maps (EKMs) and Rao-Blackwell-Kolmogorov (R-B) Filtering mechanisms for the administration of Sensor-Actuator networks (SANETs). Inspired by the BDI (Belief-Desire-Intention) Agent model from Rao and Georgeff, EKMs perform the quantitative analysis of an algorithmic artificial neural network process by using an indirect-mapping EKM to self-organize, while the Rao-Blackwell filtering mechanism reduces the external noise and interference in the problem set introduced through the self-organization process. Initial results demonstrate that a combinatorial approach to optimization with EKMs and Rao-Blackwell filtering provides an improvement in event trajectory approximation in comparison to standalone cooperative EKM processes to allow responsive event detection and optimization in patient healthcare.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/18286