Online Detection of Wi-Fi Fingerprint Alteration Strength via Deep Learning

Abstract

In Wi-Fi fingerprinting indoor localization, since signals of Wi-Fi APs vary over time, to retain high localization accuracy, the fingerprint database has to be updated periodically, which is labor-intensive and time-consuming. In this paper, we consider how to detect Wi-Fi fingerprint alteration accurately on line so as to update fingerprint database efficiently. To this end, we propose a deep learning model, AReAE (Alteration Reducing AutoEncoder), to reconstruct fingerprints from altered ones by learning alteration distribution. Based on AReAE, we further develop a FADet (Fingerprint Alteration Detection) system, which constructs a fingerprint ASmap (Alteration Strength map) with crowd-sourced Wi-Fi RSS (Received Signal Strength) measurements. The ASmap indicates how strong fingerprints change anywhere in the area of interest. FADet is verified through extensive experiments in real-world indoor scenarios. Results show that FADet yields accurate ASmaps for all scenarios tested, which can facilitate fingerprint database updating in time.