  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%.
