论文标题
使用多通道方法改善接触跟踪的接触性分类
Improving Proximity Classification for Contact Tracing using a Multi-channel Approach
论文作者
论文摘要
由于共同19个大流行，基于智能手机的接近跟踪系统引起了最大的兴趣。这些系统中的许多使用BLE信号来估计两个人之间的距离。该方法的质量取决于许多因素，因此并不总是能带来准确的结果。在本文中，我们提出了一种改善接近度分类的多通道方法，以及一个包含匹配的IEEE 802.11（2.4 GHz和5 GHz）和BLE信号强度数据的新颖的公开数据集，在四种不同的环境中测量。我们已经开发并评估了基于BLE和IEEE 802.11信号的组合分类模型。我们的方法显着改善了距离分类，因此接触跟踪的准确性也很大。在日常公共交通情景中，我们的方法能够取得良好的结果。但是，在基于IEEE 802.11探测请求的实施中，由于发送此类探针的一致性和间隔，我们还遇到了隐私问题和局限性。我们讨论了这些限制，并勾勒出如何改进我们的方法以使其适合于现实世界的部署。
Due to the COVID 19 pandemic, smartphone-based proximity tracing systems became of utmost interest. Many of these systems use BLE signals to estimate the distance between two persons. The quality of this method depends on many factors and, therefore, does not always deliver accurate results. In this paper, we present a multi-channel approach to improve proximity classification, and a novel, publicly available data set that contains matched IEEE 802.11 (2.4 GHz and 5 GHz) and BLE signal strength data, measured in four different environments. We have developed and evaluated a combined classification model based on BLE and IEEE 802.11 signals. Our approach significantly improves the distance classification and consequently also the contact tracing accuracy. We are able to achieve good results with our approach in everyday public transport scenarios. However, in our implementation based on IEEE 802.11 probe requests, we also encountered privacy problems and limitations due to the consistency and interval at which such probes are sent. We discuss these limitations and sketch how our approach could be improved to make it suitable for real-world deployment.
