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dc.contributor.authorMunir, Bilalen
dc.contributor.authorDyo, Vladimiren
dc.date.accessioned2019-09-23T09:27:05Z
dc.date.available2019-09-23T09:27:05Z
dc.date.issued2019-08-22
dc.identifier.citationMunir B, Dyo V (2019) 'Passive localization through light flicker fingerprinting', IEEE Sensors Journal, 19 (24), pp.12137-12144.en
dc.identifier.issn1530-437X
dc.identifier.doi10.1109/JSEN.2019.2936899
dc.identifier.urihttp://hdl.handle.net/10547/623499
dc.description.abstractIn this paper, we show that the flicker waveforms of various CFL and LED lamp models exhibit distinctive waveform patterns due to harmonic distortions of rectifiers and voltage regulators, the key components of modern lamp drivers. We then propose a passive localization technique based on fingerprinting these distortions that occur naturally in indoor environments and thus requires no infrastructure or additional equipment. The novel technique uses principal component analysis (PCA) to extract the most important signal features from the flicker frequency spectra followed by kNN clustering and neural net- work classifiers to identify a light source based on its flicker signature. The evaluation on 39 flicker patterns collected from 8 residential locations demonstrates that the technique can identify a location within a house with up to 90% accuracy and identify an individual house from a set of houses with an average accuracy of 86.3%.
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttps://ieeexplore.ieee.org/abstract/document/8809778en
dc.rightsGreen - can archive pre-print and post-print or publisher's version/PDF
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectsensorsen
dc.subjectmobile sensorsen
dc.subjectmachine learningen
dc.subjectInternet of Thingsen
dc.subjectmachine learning algorithmsen
dc.subjectwireless sensor networksen
dc.subjectG420 Networks and Communicationsen
dc.titlePassive localization through light flicker fingerprintingen
dc.typeArticleen
dc.identifier.eissn1558-1748
dc.identifier.journalIEEE Sensors Journalen
dc.date.updated2019-09-23T09:14:30Z
dc.description.noteUnfortunately the publisher will not allow us to archive the final version, do you have a previous version we could use, without publisher formatting applied? authors' version supplied 23/9/19
html.description.abstractIn this paper, we show that the flicker waveforms of various CFL and LED lamp models exhibit distinctive waveform patterns due to harmonic distortions of rectifiers and voltage regulators, the key components of modern lamp drivers. We then propose a passive localization technique based on fingerprinting these distortions that occur naturally in indoor environments and thus requires no infrastructure or additional equipment. The novel technique uses principal component analysis (PCA) to extract the most important signal features from the flicker frequency spectra followed by kNN clustering and neural net- work classifiers to identify a light source based on its flicker signature. The evaluation on 39 flicker patterns collected from 8 residential locations demonstrates that the technique can identify a location within a house with up to 90% accuracy and identify an individual house from a set of houses with an average accuracy of 86.3%.


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