Evaluation of machine learning models for a chipless RFID sensor tag

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dc.contributor.authorRather, Nadeemen
dc.contributor.authorSimorangkir, Roy B. V. B.en
dc.contributor.authorBuckley, Johnen
dc.contributor.authorO’Flynn, Brendanen
dc.contributor.authorTedesco, Salvatoreen
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEnterprise Irelanden
dc.contributor.funderEuropean regional development funden
dc.date.accessioned2023-07-05T11:55:18Z
dc.date.available2023-07-05T11:55:18Z
dc.date.issued2023-05-31en
dc.description.abstractRadar cross section (RCS) is a measure of the reflective strength of a radar target. Chipless RFID tags use this principle to create a tag that can be read at a distance without needing a power-hungry radio transceiver chip and/or battery. A chipless tag consists of a pattern of conductive and dielectric materials that backscatter electromagnetic (EM) waves in a distinctive pattern. A chipless tag can be read and identified by analysing the reflected waves and matching it with a predefined EM signature. In this paper, for the first time, several regression-based machine learning (ML) models are evaluated to detect identification and sensing information for an RCS-based chipless RFID tag. The simulated EM RCS signatures containing an 8-bit identification code and six capacitive sensing values are evaluated. The EM RCS signatures are evaluated within the UWB frequency band from 3.1 to 10.6 GHz. A dataset of 1,530 simulated signatures with relevant features are utilised for model training, validation, and testing. Root mean square error (RMSE) is used as the quantitative metric to evaluate their performance. It is found that Support Vector Regression (SVR) models provide the minimum RMSE for the identification code. At the same time, the Gradient Boosted Trees (GBT) regression model performed better in detecting the sensing information.en
dc.description.sponsorshipEnterprise Ireland (EI-DT20180291-A)en
dc.description.statusPeer revieweden
dc.description.versionAccepted Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRather, N., Simorangkir, R. B. V. B., Buckley, J., O’Flynn, B. and Tedesco, S. (2023) 'Evaluation of machine learning models for a chipless RFID sensor tag', 2023 17th European Conference on Antennas and Propagation (EuCAP), Florence, Italy, 26-31 March, pp. 1-5. doi: 10.23919/EuCAP57121.2023.10133043en
dc.identifier.doi10.23919/eucap57121.2023.10133043en
dc.identifier.endpage5en
dc.identifier.isbn978-1-6654-7541-9en
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/14701
dc.language.isoenen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.relation.ispartof2023 17th European Conference on Antennas and Propagation (EuCAP), Florence, Italy, 26-31 Marchen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3835/IE/VistaMilk Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/13/RC/2077/IE/CONNECT: The Centre for Future Networks & Communications/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2023, the Authors. Published by IEEE. © 2022, 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
dc.subjectChipless RFIDen
dc.subjectElectromagnetic signaturesen
dc.subjectMachine learningen
dc.subjectRadar cross sectionen
dc.subjectRegressionen
dc.subjectSupervised learningen
dc.titleEvaluation of machine learning models for a chipless RFID sensor tagen
dc.typeConference itemen
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