FiDo: Ubiquitous Fine-Grained WiFi-based Localization for Unlabelled Users via Domain Adaptation

计算机科学 自编码 分类器(UML) 适应(眼睛) 领域(数学分析) 人工智能 实时计算 域适应 数据挖掘 计算机视觉 深度学习 数学分析 数学 物理 光学
作者
Xi Chen,Hang Li,Chenyi Zhou,Xue Liu,Di Wu,Gregory Dudek
标识
DOI:10.1145/3366423.3380091
摘要

To fully support the emerging location-aware applications, location information with meter-level resolution (or even higher) is required anytime and anywhere. Unfortunately, most of the current location sources (e.g., GPS and check-in data) either are unavailable indoor or provide only house-level resolutions. To fill the gap, this paper utilizes the ubiquitous WiFi signals to establish a (sub)meter-level localization system, which employs WiFi propagation characteristics as location fingerprints. However, an unsolved issue of these WiFi fingerprints lies in their inconsistency across different users. In other words, WiFi fingerprints collected from one user may not be used to localize another user. To address this issue, we propose a WiFi-based Domain-adaptive system FiDo, which is able to localize many different users with labelled data from only one or two example users. FiDo contains two modules: 1) a data augmenter that introduces data diversity using a Variational Autoencoder (VAE); and 2) a domain-adaptive classifier that adjusts itself to newly collected unlabelled data using a joint classification-reconstruction structure. Compared to the state of the art, FiDo increases average F1 score by 11.8% and improves the worst-case accuracy by 20.2%.

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