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

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 data sets 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 multifloor buildings. To address these issues, we present a novel WiFi domain adversarial graph convolutional network 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 semisupervised 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 data set that includes multiple buildings, and the results show that it performs competitively in terms of localization accuracy in large buildings.

Automated summary

In recent years, the use of WiFi fingerprints for indoor positioning has become popular due to the availability of WiFi and mobile communication devices. However, current methods for constructing fingerprint data sets are time-consuming and often focus on ideal laboratory environments. To tackle these issues, a WiFi domain adversarial graph convolutional network model is proposed, which can be trained using a small amount of labeled data and unlabeled crowdsensed WiFi fingerprints. The model constructs heterogeneous graphs based on received signal strength indicators (RSSIs) to effectively capture the topological structure of the data, and utilizes graph convolutional networks (GCNs) to extract graph-level embeddings for improved localization accuracy in large buildings.