The StoryTeller: Scalable Building- and AP-independent Deep Learning-based Floor Prediction
The StoryTeller: Scalable Building- and AP-independent Deep Learning-based Floor Prediction
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DOI：
10.1145/3380979
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发表时间：
2020-03-01
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影响因子：
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通讯作者：
Youssef, Moustafa
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文献类型：
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作者：
Elbakly, Rizanne;Youssef, Moustafa
由于最近室内基于位置的服务的激增，对准确的楼层估计技术的需求比以往任何时候都更高，这种技术易于在任何典型的多层建筑中部署。目前试图解决地板定位问题的方法包括基于传感器的系统和3D指纹识别。然而，这些系统的部署和维护开销很高，存在传感器漂移和校准问题，和/或并非所有用户都可以使用。在本文中，我们提出了一种基于深度学习的多层建筑楼层预测技术--STORYTLER。StoryTaler利用无处不在的WiFi信号生成图像，这些图像输入卷积神经网络(CNN)，该网络经过训练，根据在可见WiFi扫描中检测到的图案预测楼层。创建输入图像，以便它们以独立于AP的方式捕获当前WiFi扫描。此外，还采用了一种新的虚拟建筑概念对信息进行规范化，使其与建筑无关。这使讲故事的人可以将经过训练的网络重新用于全新的建筑，从而显著减少部署开销。我们已经使用三个不同的建筑实施并评估了故事者，并与最先进的楼层估计技术进行了并排比较。结果表明，讲故事者可以在实际地面真实地板的一层内估计用户至少98.3%的地板。这种准确性在不同的试验台上是一致的，并且对于所使用的模型在与测试建筑完全不同的建筑中进行训练的情况下是一致的。这突出了故事人对新建筑的推广能力，以及它作为可扩展、低开销、高精度楼层定位系统的前景。
Due to the recent proliferation of location-based services indoors, the need for an accurate floor estimation technique that is easy to deploy in any typical multi-story building is higher than ever. Current approaches that attempt to solve the floor localization problem include sensor-based systems and 3D fingerprinting. Nevertheless, these systems incur high deployment and maintenance overhead, suffer from sensor drift and calibration issues, and/or are not available to all users. In this paper, we propose StoryTeller, a deep learning-based technique for floor prediction in multi-story buildings. StoryTeller leverages the ubiquitous WiFi signals to generate images that are input to a Convolutional Neural Network (CNN) which is trained to predict floors based on detected patterns in visible WiFi scans. Input images are created such that they capture the current WiFi-scan in an AP-independent manner. In addition, a novel virtual building concept is used to normalize the information in order to make them building-independent. This allows StoryTeller to reuse a trained network for a completely new building, significantly reducing the deployment overhead. We have implemented and evaluated StoryTeller using three different buildings with a side-by-side comparison with the state-of-the-art floor estimation techniques. The results show that StoryTeller can estimate the user's floor at least 98.3% within one floor of the actual ground truth floor. This accuracy is consistent across the different testbeds and for scenarios where the models used were trained in a completely different building than the tested building. This highlights StoryTeller's ability to generalize to new buildings and its promise as a scalable, low-overhead, high-accuracy floor localization system.