Virtual Graph Neural Networks: A Novel Approach for Building-Agnostic Indoor Positioning System

WiFi fingerprinting systems for indoor localization have improved drastically with the advent of deep learning. However, these systems are often designed to be used on the same testbeds they are trained on, making generalizations to unknown testbeds hard. Additionally, any changes to Access Point configurations can drastically impact system performance. These systems often require collecting new data, retraining, and/or fine-tuning, which are time-consuming and costly. To address these issues, we propose a novel localization framework that can adapt to varying environments without recalibration. This is achieved by utilizing a "virtual space" via Graph Neural Networks, enhancing adaptability and system performance.