  Mobile applications are being developed for automated logging of contacts via
Bluetooth to help scale up digital contact tracing efforts in the context of
the ongoing COVID-19 pandemic. A useful component of such applications is
inter-device distance estimation, which can be formulated as a network
localization problem. We survey several approaches and evaluate the performance
of each on real and simulated Bluetooth Low Energy (BLE) measurement datasets
with respect to both distance estimate accuracy and the proximity detection
problem. We investigate the effects of obstructions like pockets, differences
between device models, and the environment (i.e. indoors or outdoors) on
performance. We conclude that while direct estimation can provide the best
proximity detection when Received Signal Strength Indicator (RSSI) measurements
are available, network localization algorithms like Isomap, Local Linear
Embedding, and the spring model outperform direct estimation in the presence of
missing or very noisy measurements. The spring model consistently achieves the
best distance estimation accuracy.
