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

Inter-Mobile-Device Distance Estimation using Network Localization Algorithms for Digital Contact Logging Applications