Adaptability Matters: Heterogeneous Graphs for Agile Indoor Positioning in Cluttered Environments

Indoor localization has become a critical area of research with increasing relevance in applications. While numerous technologies have been explored, WiFi-based fingerprinting solutions using Received Signal Strength Indicators from multiple access points have garnered substantial attention due to the ubiquity of WiFi networks. However, these methods often encounter challenges, including fluctuation of access points and their noise signal measurements. Furthermore, they struggle to adapt effectively to cluttered or dynamically changing environments. In this paper, we introduce GraphLy: a Graph Neural Network-based model explicitly designed to tackle these challenges. GraphLy captures complex spatial relationships between different locations and adapts to environmental complexities and clutter, offering a robust solution for indoor localization. Our experiments demonstrate that GraphLy outperforms state-of-the-art WiFi-based localization techniques in two cluttered and propagation complex testbeds. In particular, we achieved a performance improvement of at least 37% and 65% in the two environments, respectively. These findings underscore the potential of GraphLy to enhance indoor localization accuracy and reliability for various real-world applications.