Fidora: Robust WiFi-Based Indoor Localization via Unsupervised Domain Adaptation
Fidora: Robust WiFi-Based Indoor Localization via Unsupervised Domain Adaptation
复制标题
DOI：
10.1109/jiot.2022.3163391
复制
发表时间：
2022-06-15
影响因子：
10.6
通讯作者：
Dudek, Gregory
中科院分区：
文献类型：
--
作者：
Chen, Xi;Li, Hang;Dudek, Gregory
新兴的物联网(IoT)应用，如无收银员购物、移动广告定向和基于地理的增强现实(AR)，有望为我们带来更多的便利和信息娱乐。为了实现这一令人惊叹的未来，我们需要随时随地为这些应用程序提供(亚)米级别分辨率的用户位置。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)米级的定位系统。对人体位置敏感的细粒度WiFi传播特征已被用于创建位置指纹。然而，这些WiFi特征也对：1)不同用户的身体形状和2)背景环境中的对象敏感。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；2)环境的日常变化，例如开门/关门。针对这一问题，本文提出了一种基于域自适应和簇假设的基于WiFi的定位系统Fidora。Fidora能够：1)仅使用一个或两个示例用户的标记数据来本地化不同的用户；2)在更改的环境中本地化同一用户，而不标记任何新数据。为了实现这些目标，Fidora集成了两个主要模块。它首先采用了一个数据增强器，该数据增强器使用了变分自动编码器(VAE)来引入数据分集。然后，它训练一个域自适应分类器，该分类器使用联合分类-重建结构来调整自己以适应新收集的未标记数据。我们进行了真实世界的实验，对照最先进的技术来评估Fidora。结果表明，在未标记用户上进行测试时，Fidora将F1平均得分提高了17.8%，将最坏情况的准确率提高了20.2%。此外，当在不同的环境中应用时，Fidora的表现比最先进的产品高出23.1%。
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%.