File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: Optimal multi-type sensor placements in Gaussian spatial fields for environmental monitoring

TitleOptimal multi-type sensor placements in Gaussian spatial fields for environmental monitoring
Authors
KeywordsMulti-type sensor placement
Submodular optimization
Gaussian process
Issue Date2019
PublisherIEEE.
Citation
4th IEEE International Smart Cities Conference (ISC2 2018), Kansas City, MO, 16-19 September 2018. In 2018 IEEE International Smart Cities Conference (ISC2), 2019 How to Cite?
AbstractAs citizens are increasingly concerned about the surrounding environment, it is important for modern cities to provide sufficient and accurate environmental information to the public for decision making in the era of smart cities. Due to limited budget, we often need to optimize the sensor placement in order to maximize the overall information gain according to certain criteria. Existing work is primarily concerned with single-type sensor placement; however, the environment usually requires accurate measurements of multiple type of environment characteristics. In this paper, we focus on the optimal multi-type sensor placement in Gaussian spatial field for environmental monitoring. We study two representative cases: the one-with-all case when each station is equipped with all types of sensors and the general case when each station is equipped with at least one type of sensor. We propose two greedy algorithms accordingly, each with a provable approximation guarantee. We evaluate the proposed approach via an application in air quality monitoring scenario in Hong Kong and experiment results demonstrate the effectiveness of the proposed approach.
DescriptionConcurrent Sessions – Session 3 : Best Paper Candidates
Persistent Identifierhttp://hdl.handle.net/10722/263546

 

DC FieldValueLanguage
dc.contributor.authorSun, C-
dc.contributor.authorYu, Y-
dc.contributor.authorLi, VOK-
dc.contributor.authorLam, JCK-
dc.date.accessioned2018-10-22T07:40:41Z-
dc.date.available2018-10-22T07:40:41Z-
dc.date.issued2019-
dc.identifier.citation4th IEEE International Smart Cities Conference (ISC2 2018), Kansas City, MO, 16-19 September 2018. In 2018 IEEE International Smart Cities Conference (ISC2), 2019-
dc.identifier.urihttp://hdl.handle.net/10722/263546-
dc.descriptionConcurrent Sessions – Session 3 : Best Paper Candidates-
dc.description.abstractAs citizens are increasingly concerned about the surrounding environment, it is important for modern cities to provide sufficient and accurate environmental information to the public for decision making in the era of smart cities. Due to limited budget, we often need to optimize the sensor placement in order to maximize the overall information gain according to certain criteria. Existing work is primarily concerned with single-type sensor placement; however, the environment usually requires accurate measurements of multiple type of environment characteristics. In this paper, we focus on the optimal multi-type sensor placement in Gaussian spatial field for environmental monitoring. We study two representative cases: the one-with-all case when each station is equipped with all types of sensors and the general case when each station is equipped with at least one type of sensor. We propose two greedy algorithms accordingly, each with a provable approximation guarantee. We evaluate the proposed approach via an application in air quality monitoring scenario in Hong Kong and experiment results demonstrate the effectiveness of the proposed approach.-
dc.languageeng-
dc.publisherIEEE.-
dc.relation.ispartof2018 IEEE International Smart Cities Conference (ISC2)-
dc.rights2018 IEEE International Smart Cities Conference (ISC2). Copyright © IEEE.-
dc.rights©2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.subjectMulti-type sensor placement-
dc.subjectSubmodular optimization-
dc.subjectGaussian process-
dc.titleOptimal multi-type sensor placements in Gaussian spatial fields for environmental monitoring-
dc.typeConference_Paper-
dc.identifier.emailLi, VOK: vli@eee.hku.hk-
dc.identifier.emailLam, JCK: h9992013@hkucc.hku.hk-
dc.identifier.authorityLi, VOK=rp00150-
dc.identifier.authorityLam, JCK=rp00864-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/ISC2.2018.8656676-
dc.identifier.hkuros294329-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats