Graph-based Fingerprint Update Using Unlabelled WiFi Signals

2025-07-15

AI Summary

WiFi fingerprint databases rapidly become outdated due to AP additions/removals and transmit power variations. To address this, we propose GUFU—a novel framework that pioneers the integration of graph neural networks (GNNs) with link prediction for incremental, label-free fingerprint database self-updating using crowdsourced RSS measurements. GUFU dynamically constructs a location-AP relational graph based on RSS similarity, enabling retraining of fingerprint embeddings and recalibration of location mappings without manual labeling. Evaluated across four large-scale real-world deployments, GUFU reduces RSS prediction error by 21.4% and localization error by 29.8% over the best baseline, significantly enhancing long-term robustness and adaptability. This work overcomes the fundamental limitations of conventional fingerprint updating—namely, reliance on costly ground-truth annotations or periodic full-scale resampling—and establishes a new paradigm for continuous indoor localization in dynamic wireless environments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Machine Learning: Graph-based Machine Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

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.

Problem

• Updating WiFi fingerprint database using unlabelled signals
• Incorporating new APs without discarding their features
• Improving fingerprint adaptivity with graph-based approach

Innovation

• Graph-based update using unlabelled WiFi signals
• GNN and link prediction for incremental retraining
• Enhances fingerprint adaptivity with new APs