Efficient spatio-temporal sensor deployments: A smart building application

Nguyen, L; Hu, G; Spanos, CJ

Efficient spatio-temporal sensor deployments: A smart building application

Nguyen, L

Hu, G Spanos, CJ

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Publication Type:: Conference Proceeding
Citation:: IEEE International Conference on Control and Automation, ICCA, 2017, pp. 612 - 617
Issue Date:: 2017-08-04

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Field	Value	Language
dc.contributor.author	Nguyen, L https://orcid.org/0000-0001-5360-886X	en_US
dc.contributor.author	Hu, G	en_US
dc.contributor.author	Spanos, CJ	en_US
dc.date.issued	2017-08-04	en_US
dc.identifier.citation	IEEE International Conference on Control and Automation, ICCA, 2017, pp. 612 - 617	en_US
dc.identifier.isbn	9781538626795	en_US
dc.identifier.issn	1948-3449	en_US
dc.identifier.uri	http://hdl.handle.net/10453/115217
dc.description.abstract	© 2017 IEEE. The paper addresses the problem of efficiently deploying sensors in spatial environments, e.g. smart buildings, for the purpose of monitoring environmental phenomena. By modelling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality criterion of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is not affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment problem is then solved by a practically and feasibly polynomial algorithm, where its solutions are guaranteed. The proposed approaches were implemented in a real tested space in a university building, where the obtained results are highly promising.	en_US
dc.relation.ispartof	IEEE International Conference on Control and Automation, ICCA	en_US
dc.relation.isbasedon	10.1109/ICCA.2017.8003130	en_US
dc.title	Efficient spatio-temporal sensor deployments: A smart building application	en_US
dc.type	Conference Proceeding
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

© 2017 IEEE. The paper addresses the problem of efficiently deploying sensors in spatial environments, e.g. smart buildings, for the purpose of monitoring environmental phenomena. By modelling the environmental fields using spatio-temporal Gaussian processes, a new and efficient optimality criterion of minimizing prediction uncertainties is proposed to find the best sensor locations. Though the environmental processes spatially and temporally vary, the proposed approach of choosing sensor positions is not affected by time variations, which significantly reduces computational complexity of the optimization problem. The sensor deployment problem is then solved by a practically and feasibly polynomial algorithm, where its solutions are guaranteed. The proposed approaches were implemented in a real tested space in a university building, where the obtained results are highly promising.

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http://hdl.handle.net/10453/115217