GConvLoc: WiFi Fingerprinting-Based Indoor Localization using Graph Convolutional Networks

Abstract

We propose GConvLoc, a WiFi fingerprinting-based in-door localization method utilizing graph convolutional networks. Using the graph structure, we can consider the fingerprint data of the reference points and their location labels in addition to the fingerprint data of the test point at inference time. Experimental results show that GConvLoc outperforms baseline methods that do not utilize graphs.