Smartphone apps for exposure notification and contact tracing have been shown to be effective in controlling the COVID-19 pandemic. However, Bluetooth Low Energy tokens similar to those broadcast by existing apps can still be picked up far away from the transmitting device. In this paper, we present a new class of methods for detecting whether or not two Wi-Fi-enabled devices are in immediate physical proximity, i.e. 2 or fewer meters apart, as established by the U.S. Centers for Disease Control and Prevention (CDC). Our goal is to enhance the accuracy of smartphone-based exposure notification and contact tracing systems. We present a set of binary machine learning classifiers that take as input pairs of Wi-Fi RSSI fingerprints. We empirically verify that a single classifier cannot generalize well to a range of different environments with vastly different numbers of detectable Wi-Fi Access Points (APs). However, specialized classifiers, tailored to situations where the number of detectable APs falls within a certain range, are able to detect immediate physical proximity significantly more accurately. As such, we design three classifiers for situations with low, medium, and high numbers of detectable APs. These classifiers distinguish between pairs of RSSI fingerprints recorded 2 or fewer meters apart and pairs recorded further apart but still in Bluetooth range. We characterize their balanced accuracy for this task to be between 66.8% and 77.8%.

翻译：接触通知和接触追踪的智能智能应用软件已证明在控制COVID-19大流行病方面是有效的。然而,与现有应用程序所播放的类似蓝牙低能标志仍然可以远离传输设备。在本文中,我们展示了一种新的方法,用以检测两个无线接入装置是否直接接近实际距离,即美国疾病控制和预防中心(疾病控制和预防中心)规定的2米或更小米距离。我们的目标是提高智能手机接触通知和接触追踪系统的准确性。我们展示了一套二元机器学习分类器,这些分类器可以作为Wi-Fi RSSI指纹的输入配对。我们从经验上核实,单一的分类器无法对不同环境进行广泛概括,其可检测到的Wi-Fi接入点的数量大不相同。然而,专门分类器适合可检测到的AP数量在一定范围内的情况,能够非常准确地检测到直接接近实际的距离。我们为低、中、中、高RISI指纹配置的三种情况设计了分级分级器,在低、中、低至低的RISI级之间,这些分级数为低、低的分级数。我们所记录到低级的RISBRISL8和高级。