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

Visible Light Communication (VLC) systems provide not only illumination and data communications, 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 (CSI) that a VLC receiver computes to equalize the OFDM subcarriers can be 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, such as the position of an object in the sensing area. When compared to supervised learning algorithms, the proposed approach does not need tags in the training data, simplifying notably the implementation. The validation of this monitoring approach was done using a software-defined VLC transmission based on OFDM, in which a reflected copy of the intensity modulated signal coming from a white LED was captured by a pair of photodetectors. The experimental evaluation of the 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 sensor on the object to-be-tracked.