Graph-based Fingerprint Update Using Unlabelled WiFi Signals

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


MPhil Thesis Defence


Title: "Graph-based Fingerprint Update Using Unlabelled WiFi Signals"

By

Mr. Ka Ho CHIU


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:                   Tuesday, 26 August 2025

Time:                   10:00am - 12:00noon

Venue:                  Room 2611
                        Lifts 31/32

Chairman:               Prof. Pedro SANDER

Committee Members:      Prof. Gary CHAN (Supervisor)
                        Prof. Song GUO