Locational optimization based sensor placement for monitoring Gaussian processes modeled spatial phenomena

Van Nguyen, L; Kodagoda, S; Ranasinghe, R; Dissanayake, G

Locational optimization based sensor placement for monitoring Gaussian processes modeled spatial phenomena

Van Nguyen, L

Kodagoda, S

Ranasinghe, R

Dissanayake, G

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013, 2013, pp. 1706 - 1711
Issue Date:: 2013-08-19

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Field	Value	Language
dc.contributor.author	Van Nguyen, L https://orcid.org/0000-0001-5360-886X	en_US
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138	en_US
dc.contributor.author	Ranasinghe, R https://orcid.org/0000-0002-3785-3723	en_US
dc.contributor.author	Dissanayake, G https://orcid.org/0000-0002-7992-0680	en_US
dc.date.issued	2013-08-19	en_US
dc.identifier.citation	Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013, 2013, pp. 1706 - 1711	en_US
dc.identifier.isbn	9781467363211	en_US
dc.identifier.uri	http://hdl.handle.net/10453/23761
dc.description.abstract	This paper addresses the sensor placement problem associated with monitoring spatial phenomena, where mobile sensors are located on the optimal sampling paths yielding a lower prediction error. It is proposed that the spatial phenomenon to be monitored is modeled using a Gaussian Process and a variance based density function is employed to develop an expected-value function. A locational optimization based effective algorithm is employed to solve the resulting minimization of the expected-value function. We designed a mutual information based strategy to select the most informative subset of measurements effectively with low computational time. Our experimental results on real-world datasets have verified the superiority of the proposed approach. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2013 IEEE 8th Conference on Industrial Electronics and Applications, ICIEA 2013	en_US
dc.relation.isbasedon	10.1109/ICIEA.2013.6566643	en_US
dc.title	Locational optimization based sensor placement for monitoring Gaussian processes modeled spatial phenomena	en_US
dc.type	Conference Proceeding
utslib.for	091303 Autonomous Vehicles	en_US
dc.location.activity	Melbourne, Australia	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 Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CAS - Centre for Autonomous Systems
utslib.copyright.status	open_access
pubs.publication-status	Published	en_US

Abstract:

This paper addresses the sensor placement problem associated with monitoring spatial phenomena, where mobile sensors are located on the optimal sampling paths yielding a lower prediction error. It is proposed that the spatial phenomenon to be monitored is modeled using a Gaussian Process and a variance based density function is employed to develop an expected-value function. A locational optimization based effective algorithm is employed to solve the resulting minimization of the expected-value function. We designed a mutual information based strategy to select the most informative subset of measurements effectively with low computational time. Our experimental results on real-world datasets have verified the superiority of the proposed approach. © 2013 IEEE.

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http://hdl.handle.net/10453/23761