HueLoc

Localization Through LEDs’ Hue Spectrum

Journal Article (2024)
Author(s)

Jagdeep Singh (Toshiba Europe, TU Delft - Embedded Systems)

Marco Zúñiga Zuñiga Zamalloa (TU Delft - Networked Systems, TU Delft - Software Technology)

Tim Farnham (Toshiba Europe)

Qing Wang (TU Delft - Embedded Systems, TU Delft - Software Technology)

Research Group
Networked Systems
DOI related publication
https://doi.org/10.1109/JIOT.2024.3512943
More Info
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Publication Year
2024
Language
English
Research Group
Networked Systems
Bibliographical Note
Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public. @en
Issue number
7
Volume number
12
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Abstract

Over the past decade, visible light positioning has become increasingly important for precise localization systems, yet its widespread adoption is limited due to the necessity of modifying existing lighting systems. This paper presents HueLoc, a novel method that bypasses this issue by using inherent features of light, such as the dominant colours in white LED lights, and employs affordable, energy-efficient hue sensors for location services. We propose that by extracting the power at dominant wavelengths of LEDs, these can be uniquely identified using a specifically designed signature. The unique signatures can be used by mobile objects for spatial awareness and further localization using the proposed regression-based learning approach. Our experiments demonstrate that HueLoc attains a location-mapping accuracy of 100% and achieves decimeter-level localization precision with a moving object in uncontrolled lighting conditions. Moreover, these unique signatures can be combined with other RF-based technologies to enhance their localization accuracy. As an example, this paper details the integration of Bluetooth features with light signatures using a three-stage incremental learning approach.

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