GraFin: An Applicable Graph-based Fingerprinting Approach for Robust Indoor Localization

Abstract

Wi-Fi fingerprinting using the received signal strength (RSS) of the access point (AP) as a physical signal feature is widely studied in the indoor localization area with various applications. One main problem with fingerprinting based approach is the uncertainty of RSS measurements, which often leads to instability and decline of localization performance. In this work, we propose GraFin, a graph-based fingerprinting approach, to provide accurate and robust indoor localization without tedious site surveys and extra assistant information. The key idea lies in the insight that despite the RSS measurement of one AP at one reference point (RP) can be noisy, the proximity pattern, which describes one AP's relative position to other APs and RPs, is usually more stable. Specifically, GraFin models APs and RPs on a graph based on limited RSS measurements and provides position-aware fingerprints for APs and RPs based on an inductive deep graph model. We evaluate GraFin on a public indoor localization dataset, and the results demonstrate the effectiveness and robustness of our approach. Furthermore, we apply our approach to the arrival-departure time estimation task for instant delivery service. Experiment results on the enterprise dataset from one of the largest instant delivery platforms in China show that GraFin outperforms baseline approaches with significantly lower time estimation error.