"For indoor settings, we investigate the impact of location on the spectral distribution of the received light, i.e., the intensity of light for different wavelengths. Our investigations confirm that even under the same light source, different locations exhibit slightly different spectral distribution due to reflections from their localised environment containing different materials or colours. By exploiting this observation, we propose Spectral-Loc, a novel indoor localization system that uses light spectral information to identify the location of the device. With spectral sensors finding their way into the latest products and applications, such as white balancing in smartphone photography, Spectral-Loc can be readily deployed without requiring any additional hardware or infrastructure. We prototype Spectral-Loc using a commercial-off-the-shelf light spectral sensor, AS7265x, which can measure light intensity over 18 different wavelength sub-bands. We benchmark the localization accuracy of Spectral-Loc against the conventional light intensity sensors that provide only a single intensity value. Our evaluations over two different indoor spaces, a meeting room, and a large office space, demonstrate that the use of light spectral information significantly reduces the localization error for the different percentiles. https://dl.acm.org/doi/10.1145/3580878"

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https://hci.top/en/papers/ubicomp/128359/2023

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At a Glance

Paper Snapshot

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Source
UbiComp
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Year
2023
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No award tagged
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Authors
8 authors
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Subtopics
Geospatial & Map Visualization, Context-Aware Computing
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Professions
Software Engineers & Developers, UI/UX Designers, Urban Planners
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Content Status
Abstract only
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Related Papers
1 related papers