Improving Proximity Classification for Contact Tracing using a Multi-channel Approach

Due to the COVID-19 pandemic, smartphone-based proximity tracing systems became of utmost interest. Many of these systems use Bluetooth Low Energy (BLE) signal strength data to estimate the distance between two persons. The quality of this method depends on many factors and, therefore, does hardly deliver accurate results. 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 & 5 GHz) and BLE signal strength data, measured in four different environments. We utilize these data to train machine learning models. The evaluation showed significant improvements in the distance classification and consequently also the contact tracing accuracy. However, 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.