A Geometric Deep Learning Framework for Accurate Indoor Localization

Recent advances in (deep) machine learning offer new opportunities to solve indoor fingerprint-based localization problems. However, the majority of localization solutions employing popular machine learning models, such as k-nearest neighbors (
$k$
-NN), support vector machine (SVM), multi-layer perceptron (MLP), and convolutional neural network (CNN), do not sufficiently realize inability of these models to fully represent the non-Euclidean nature of fingerprint data, which consequently degrades their performance. In this paper, we first explain how these commonly-used models fail to effectively encode the fingerprint data due to their assumption (or lack of it) regarding fingerprints and/or geometric and topology information hidden within the RSSI measurements. Based on this, we provide our motivation to use geometric deep learning for indoor fingerprint-based localization. We then present a systematic approach to transform fingerprints into graphs, accounting for the co-existence of multiple radio frequency signal technologies. Finally, we present our localization approach based on a GraphSAGE estimator. Through extensive performance evaluation, using two different case studies (datasets), we show to what extent our proposed localization approach improves upon the state-of-the-art localization solutions. We also conclude that the best configuration of our approach requires both the edge features in the graphs and the pooling aggregator in the GraphSAGE model.