Passive localization through light flicker fingerprinting

Munir, Bilal; Dyo, Vladimir

dc.contributor.author	Munir, Bilal	en
dc.contributor.author	Dyo, Vladimir	en
dc.date.accessioned	2019-09-23T09:27:05Z
dc.date.available	2019-09-23T09:27:05Z
dc.date.issued	2019-08-22
dc.identifier.citation	Munir B, Dyo V (2019) 'Passive localization through light flicker fingerprinting', IEEE Sensors Journal, 19 (24), pp.12137-12144.	en
dc.identifier.issn	1530-437X
dc.identifier.doi	10.1109/JSEN.2019.2936899
dc.identifier.uri	http://hdl.handle.net/10547/623499
dc.description.abstract	In 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.iso	en	en
dc.publisher	IEEE	en
dc.relation.url	https://ieeexplore.ieee.org/abstract/document/8809778	en
dc.rights	Green - can archive pre-print and post-print or publisher's version/PDF
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/4.0/	*
dc.subject	sensors	en
dc.subject	mobile sensors	en
dc.subject	machine learning	en
dc.subject	Internet of Things	en
dc.subject	machine learning algorithms	en
dc.subject	wireless sensor networks	en
dc.subject	G420 Networks and Communications	en
dc.title	Passive localization through light flicker fingerprinting	en
dc.type	Article	en
dc.identifier.eissn	1558-1748
dc.identifier.journal	IEEE Sensors Journal	en
dc.date.updated	2019-09-23T09:14:30Z
dc.description.note	Unfortunately 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.abstract	In 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%.