Internet of Things
machine learning algorithms
wireless sensor networks
G420 Networks and Communications
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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%.
CitationMunir B, Dyo V (2019) 'Passive localization through light flicker fingerprinting', IEEE Sensors Journal, 19 (24), pp.12137-12144.
JournalIEEE Sensors Journal
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