Fidora: Robust WiFi-Based Indoor Localization via Unsupervised Domain Adaptation

Emerging Internet of Things (IoT) applications, such as cashier-less shopping, mobile ads targeting, and geo-based augmented reality (AR), are expected to bring us much more convenience and infotainment. To realize this amazing future, we need to feed these applications with user locations of (sub) meter-level resolution anytime and anywhere. Unfortunately, many widely used location sources are either unavailable indoor (e. g. , global positioning system) or coarse grained (e. g. , user check-ins). In order to provide ubiquitous localization services, the widespread WiFi signals are being leveraged to establish (sub) meter-level localization systems. Fine-grained WiFi propagation characteristics, which are sensitive to human body locations, have been employed to create location fingerprints. However, these WiFi characteristics are also sensitive to: 1) the body shapes of different users and 2) the objects in the background environment. Consequently, systems based on WiFi fingerprints are vulnerable in the presence of: 1) new users with different body shapes and 2) daily changes of the environment, e. g. , opening/closing doors. To tackle this issue, this article proposes a WiFi-based localization system based on domain-adaptation with cluster assumption, named Fidora. Fidora is able to: 1) localize different users with labeled data from only one or two example users and 2) localize the same user in a changed environment without labeling any new data. To achieve these, Fidora integrates two major modules. It first adopts a data augmenter that introduces data diversity using a variational autoencoder (VAE). It then trains a domain-adaptive classifier that adjusts itself to newly collected unlabeled data using a joint classification-reconstruction structure. We conducted real-world experiments to evaluate Fidora against the state of the art. It is demonstrated that when tested on an unlabeled user, Fidora increases the average F1 score by 17. 8% and improves the worst case accuracy by 20. 2%. Moreover, when applied in a varied environment, Fidora outperforms the state of the art by 23. 1%.