Practical WiFi Indoor Localization: Unleashing the Potential of GNNs for Accuracy and Robustness

WiFi-based indoor localization, supported by comprehensive infrastructure, is considered a highly promising solution. However, practical applications of existing WiFi-based methods often struggle due to dynamic antenna configurations and potential influence from obstacles, which can undermine the reliability of channel state information and frustrate localization. Even worse, environmental changes may lead to a domain shift, further degrading localization accuracy and system robustness. To address these problems, this paper introduces GraphFi, a novel system that leverages graph neural networks (GNNs) to deliver accurate and robust localization using nearby access points (APs). GraphFi designs two types of graph structures: intra-AP graph and inter-AP graph, to maximize the use of information from all available APs. They aggregate the local features among antennas within each AP and global features across APs, effectively addressing the problem of dynamic antenna configurations. Two specialized GNNs are utilized to derive accurate user locations from these graphs. Additionally, we introduce an anomaly detection method to identify and exclude obstacle-affected APs. This method also employs a tailored GNN to mitigate influence from unpredictable obstacles. Furthermore, we integrate an unsupervised domain adaptation mechanism based on a gradient reversal layer into GNNs. This helps maintain localization performance in a cost-efficient manner and ensures sustained effectiveness in a cross-domain setting. We prototype GraphFi using commodity WiFi devices and conduct extensive experiments in various scenarios. The results demonstrate that GraphFi achieves average localization errors of 0.17 m in a single-domain setting and 0.2851 m in a cross-domain setting, surpassing existing solutions in both precision and robustness.