Visible light communication-based monitoring for indoor environments using unsupervised learning

TL;DR

This paper presents a VLC-based indoor monitoring system using unsupervised learning on Channel State Information, achieving centimeter-level positioning accuracy without extensive sensor deployment.

Contribution

The study introduces an unsupervised learning approach for VLC-based indoor monitoring that simplifies implementation by eliminating the need for labeled training data.

Findings

Achieved positioning accuracy in the few-centimeter range.

No need for large sensor deployment or object-tagging.

Validated with a practical VLC link using OFDM and photodetectors.

Abstract

Visible Light Communication~(VLC) systems provide not only illumination and data communication, but also indoor monitoring services if the effect that different events create on the received optical signal is properly tracked. For this purpose, the Channel State Information that a VLC receiver computes to equalize the subcarriers of the OFDM signal can be also reused to train an Unsupervised Learning classifier. This way, different clusters can be created on the collected CSI data, which could be then mapped into relevant events to-be-monitored in the indoor environments, such as the presence of a new object in a given position or the change of the position of a given object. When compared to supervised learning algorithms, the proposed approach does not need to add tags in the training data, simplifying notably the implementation of the machine learning classifier. The practical validation the monitoring approach was done with the aid of a software-defined VLC link based on OFDM, in which a copy of the intensity modulated signal coming from a Phosphor-converted LED was captured by a pair of Photodetectors~(PDs). The performance evaluation of the experimental VLC-based monitoring demo achieved a positioning accuracy in the few-centimeter-range, without the necessity of deploying a large number of sensors and/or adding a VLC-enabled sensor on the object to-be-tracked.