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

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