使用多通道方法改进接触追踪的邻近分类
Improving Proximity Classification for Contact Tracing using a Multi-channel Approach
AI总结：
针对 BLE 接触追踪距离估计不准的问题，论文提出融合 BLE 与双频 IEEE 802.11 信号的多通道分类模型及配套数据集，显著提升公共交通场景的邻近分类与接触追踪精度。
摘要：
由于 COVID-19 大流行，基于智能手机的邻近追踪系统成为备受关注的焦点。许多此类系统使用 BLE 信号来估计两人之间的距离。这种方法的质量取决于许多因素，因此并不总能给出准确结果。本文提出一种用于改进邻近分类的多通道方法，以及一个新颖的公开可用数据集，其中包含在四种不同环境中测量的、相互匹配的 IEEE 802.11（2.4 GHz 和 5 GHz）与 BLE 信号强度数据。我们开发并评估了一个基于 BLE 和 IEEE 802.11 信号的组合分类模型。我们的方法显著改进了距离分类，从而也提高了接触追踪准确性。借助该方法，我们能够在日常公共交通场景中取得良好结果。然而，在我们基于 IEEE 802.11 探测请求的实现中，由于此类探测发送的一致性和时间间隔，我们也遇到了隐私问题和限制。我们讨论了这些限制，并概述如何改进该方法，使其适用于现实世界部署。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
