Graph-based Fingerprint Update Using Unlabelled WiFi Signals

TL;DR

This paper introduces GUFU, a graph neural network-based method for updating WiFi fingerprint databases with unlabelled signals, effectively handling new access points and improving localization accuracy.

Contribution

The paper presents a novel graph-based approach, GUFU, that effectively updates WiFi fingerprints using unlabelled signals and new APs, outperforming existing methods.

Findings

Achieves 21.4% reduction in RSS error

Achieves 29.8% reduction in location prediction error

Demonstrates effectiveness across four large sites

Abstract

WiFi received signal strength (RSS) environment evolves over time due to movement of access points (APs), AP power adjustment, installation and removal of APs, etc. We study how to effectively update an existing database of fingerprints, defined as the RSS values of APs at designated locations, using a batch of newly collected unlabelled (possibly 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 address 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 (GNN) and a link 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 representative 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.