Indoor Localization using Graph Neural Networks

Abstract

The topic of indoor localization is very relevant today as it provides solutions in different applications (e.g. shopping malls or museums). We consider here the so-called Wi-Fi fingerprinting approach, where RSSI measurements from the access points are used to locate the device into certain predefined areas. Typically, this mapping from measurements to area is obtained by training a machine learning algorithm. However, traditional techniques do not take into account the underlying geometry of the problem. We thus investigate here a novel approach: using machine learning techniques in graphs, in particular Graph Neural Networks. We propose a way to construct the graph using only the RSSI measurements (and not the floor plan) and evaluate the resulting algorithm on two real datasets. The results are very encouraging, showing a better performance than existing methods, in some cases even using a much smaller amount of training data.