Domain Adversarial Graph Convolutional Network Based on RSSI and Crowdsensing for Indoor Localization

TL;DR

This paper introduces a semi-supervised domain adversarial graph convolutional network that leverages RSSI and crowdsensed data for efficient indoor localization, reducing data collection efforts and improving accuracy across multiple buildings.

Contribution

The novel WiDAGCN model effectively uses limited labeled data and extensive unlabeled crowdsensed data with graph-based and adversarial training techniques for indoor localization.

Findings

Achieves competitive localization accuracy in large multi-building environments.

Effectively utilizes unlabeled crowdsensed WiFi fingerprints for training.

Reduces the need for labor-intensive data collection.

Abstract

In recent years, the use of WiFi fingerprints for indoor positioning has grown in popularity, largely due to the widespread availability of WiFi and the proliferation of mobile communication devices. However, many existing methods for constructing fingerprint datasets rely on labor-intensive and time-consuming processes of collecting large amounts of data. Additionally, these methods often focus on ideal laboratory environments, rather than considering the practical challenges of large multi-floor buildings. To address these issues, we present a novel WiDAGCN model that can be trained using a small number of labeled site survey data and large amounts of unlabeled crowdsensed WiFi fingerprints. By constructing heterogeneous graphs based on received signal strength indicators (RSSIs) between waypoints and WiFi access points (APs), our model is able to effectively capture the topological structure of the data. We also incorporate graph convolutional networks (GCNs) to extract graph-level embeddings, a feature that has been largely overlooked in previous WiFi indoor localization studies. To deal with the challenges of large amounts of unlabeled data and multiple data domains, we employ a semi-supervised domain adversarial training scheme to effectively utilize unlabeled data and align the data distributions across domains. Our system is evaluated using a public indoor localization dataset that includes multiple buildings, and the results show that it performs competitively in terms of localization accuracy in large buildings.