Graph-based Fingerprint Update Using Unlabelled WiFi Signals

Abstract

WiFi received signal strength (RSS) environment evolves over time due to factors such as movement of access points (APs), AP power adjustment, installation and removal. We study how to effectively update existing fingerprints, defined as the RSS values of APs at designated locations, using a batch of newly collected unlabelled crowdsourced WiFi signals. Prior art either estimates the locations of the new signals without updating the existing fingerprints or filters out the new APs without sufficiently embracing their features. To overcome that, we propose GUFU, a novel effective graph-based approach to update WiFi fingerprints using unlabelled signals with possibly new APs. Based on the observation that similar signal vectors likely imply physical proximity, GUFU employs a graph neural network and an edge prediction algorithm to retrain an incremental network given the new signals and APs. After the retraining, it then updates the signal vectors at the designated locations. Through extensive experiments in four large sites, GUFU is shown to achieve remarkably higher fingerprint adaptivity as compared with other state-of-the-art approaches, with error reduction of 21.4% and 29.8% in RSS values and location prediction, respectively.

Date of Award	2025
Original language	English
Awarding Institution	The Hong Kong University of Science and Technology
Supervisor	Gary Shueng Han CHAN (Supervisor)