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%.

中文翻译：

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Fidora：通过无监督域适应实现基于 WiFi 的稳健室内定位


新兴的物联网 (IoT) 应用，例如无收银员购物、移动广告定位和基于地理的增强现实 (AR)，预计将为我们带来更多便利和信息娱乐。为了实现这个美好的未来，我们需要随时随地为这些应用程序提供（亚）米级分辨率的用户位置。不幸的是，许多广泛使用的位置源要么在室内不可用（例如，全球定位系统），要么是粗粒度的（例如，用户签到）。为了提供无处不在的定位服务，人们正在利用广泛的 WiFi 信号来建立亚米级定位系统。对人体位置敏感的细粒度 WiFi 传播特性已被用来创建位置指纹。然而，这些WiFi特性也对：1）不同用户的体形和2）背景环境中的物体敏感。因此，基于 WiFi 指纹的系统在以下情况下很容易受到攻击：1）具有不同体型的新用户和 2）环境的日常变化，例如打开/关闭门。为了解决这个问题，本文提出了一种基于具有集群假设的域适应的WiFi定位系统，名为Fidora。 Fidora 能够：1）仅使用来自一两个示例用户的标记数据来本地化不同用户，2）在更改的环境中本地化同一用户，而不标记任何新数据。为了实现这些，Fidora 集成了两个主要模块。它首先采用数据增强器，使用变分自动编码器（VAE）引入数据多样性。然后，它训练一个域自适应分类器，该分类器使用联合分类重建结构调整自身以适应新收集的未标记数据。 我们进行了真实世界的实验，根据现有技术来评估 Fidora。结果表明，在对未标记用户进行测试时，Fidora 将平均 $F1$ 分数提高了 17.8%，并将最坏情况下的准确率提高了 20.2%。此外，当应用于不同的环境时，Fidora 的性能比现有技术高出 23.1%。