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Enabling Body-Centric Computing Applications with LED-to-Camera Communication

Authors: Omer Dalgic; Daniele Puccinelli; Marco Zuniga;

Enabling Body-Centric Computing Applications with LED-to-Camera Communication

Abstract

Advances in Visible Light Communication are enabling novel Internet of Things applications. Going forward, we expect that LED-to-Camera links will enable a wide range of body-centric computing applications. Up until now, most LED-to-Camera studies have been following a deploy-and-test approach instead of a principled methodology. This ad-hoc design raises up two problems. First, we cannot compare fairly the various methods proposed in the literature because they use different types of LEDs and cameras. Second, and perhaps more importantly, we cannot identify the fundamental opportunities and limits of these novel links. To overcome these challenges, we propose a simple analytical model that estimates the range and data rate of LED-to-camera links prior to deployment. The model is built from first principles and requires only a limited set of parameters. To validate the accuracy of our model, we consider the two main transmission modes used in the literature: binary transmission and communication based on the rolling shutter effect. Our experimental evaluation confirms the predictions of the analytical model

Country
Netherlands
Keywords

visible light communication, analytical model, camera

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
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3
Top 10%
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17
41
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