Name
Abstract | ||
|---|---|---|
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
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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%. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JIOT.2022.3163391 | IEEE Internet of Things Journal |
Keywords | DocType | Volume |
Location awareness,transfer learning,unsupervised learning | Journal | 9 |
Issue | ISSN | Citations |
12 | 2327-4662 | 0 |
PageRank | References | Authors |
0.34 | 40 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xi Chen | 1 | 0 | 0.68 |
| Hang Li | 2 | 0 | 1.35 |
| Chenyi Zhou | 3 | 6 | 0.72 |
| Xue Liu | 4 | 6 | 2.75 |
| Di Wu | 5 | 636 | 117.73 |
| Gregory Dudek | 6 | 7 | 6.16 |