Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/104436
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dc.contributorDepartment of Industrial and Systems Engineering-
dc.creatorTsang, YPen_US
dc.creatorChoy, KLen_US
dc.creatorWu, CHen_US
dc.creatorHo, GTSen_US
dc.date.accessioned2024-02-05T08:49:52Z-
dc.date.available2024-02-05T08:49:52Z-
dc.identifier.issn1089-7798en_US
dc.identifier.urihttp://hdl.handle.net/10397/104436-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rights© 2019 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.en_US
dc.rightsThe following publication Tsang, Y. P., Choy, K. L., Wu, C. H., & Ho, G. T. S. (2019). Multi-Objective Mapping Method for 3D Environmental Sensor Network Deployment. IEEE Communications Letters, 23(7), 1231–1235 is available at https://doi.org/10.1109/LCOMM.2019.2914440.en_US
dc.subjectEnvironmental sensor networken_US
dc.subjectGenetic algorithmen_US
dc.subjectMapping methoden_US
dc.subjectMulti-objective optimizationen_US
dc.subjectMulti-responses Taguchi methoden_US
dc.titleMulti-objective mapping method for 3D environmental sensor network deploymenten_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage1231en_US
dc.identifier.epage1235en_US
dc.identifier.volume23en_US
dc.identifier.issue7en_US
dc.identifier.doi10.1109/LCOMM.2019.2914440en_US
dcterms.abstractEffective deployment of the emerging environmental sensor network in environmental mapping has become essential in numerous industrial applications. The essential factors for deployment include cost, coverage, connectivity, airflow of heating, ventilation, and air conditioning, system lifetime, and fault tolerance. In this letter, a three-stage deployment scheme is proposed to formulate the above-mentioned considerations, and the fuzzy temperature window is established to adjust sensor activation times over various ambient temperatures. To optimize the deployment effectively, a multi-response Taguchi-guided k-means clustering is proposed to embed in the genetic algorithm, where an improved set of the initial population is formulated and system parameters are optimized. Therefore, the computational time for repeated deployment is shortened, while the solution convergence can be improved.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationIEEE communications letters, July 2019, v. 23, no. 7, p. 1231-1235en_US
dcterms.isPartOfIEEE communications lettersen_US
dcterms.issued2019-07-
dc.identifier.scopus2-s2.0-85068832992-
dc.identifier.eissn1558-2558en_US
dc.identifier.artn8705334en_US
dc.description.validate202402 bcch-
dc.description.oaAccepted Manuscripten_US
dc.identifier.FolderNumberISE-0460-
dc.description.fundingSourceSelf-fundeden_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS53188915-
dc.description.oaCategoryGreen (AAM)en_US
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