  The rapid increase in demand for wireless controlled Smart Lighting has
created a need to automate the mapping between the identifiers for individual
light sources and their physical locations. To control Smart Lights, their IDs
and physical locations relative to each other must be determined. Nowadays,
skilled technicians perform this process manually, which requires a lot of
effort, is time-consuming, and incurs high costs, particularly with
non-stationary lights. Visible Light Communication has been presented as a
possible solution to this problem. This paper presents an approach based on
Visible Light Communication that leverages Machine Learning to automate the
mapping process between the identifiers and the relative physical location of
Smart Lights. We show that our approach provides a better location-mapping
performance compared to existing methods.
