  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.
