Improving Proximity Classification for Contact Tracing using a Multi-channel Approach
This work addresses the need for more reliable contact tracing systems during pandemics like COVID-19, though it is incremental as it builds on existing signal-based methods.
The paper tackled the problem of inaccurate proximity classification in smartphone-based contact tracing by developing a multi-channel approach using combined BLE and IEEE 802.11 signals, which significantly improved distance classification accuracy, with good results in public transport scenarios.
Due to the COVID 19 pandemic, smartphone-based proximity tracing systems became of utmost interest. Many of these systems use BLE signals to estimate the distance between two persons. The quality of this method depends on many factors and, therefore, does not always deliver accurate results. In this paper, 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 GHz and 5 GHz) and BLE signal strength data, measured in four different environments. We have developed and evaluated a combined classification model based on BLE and IEEE 802.11 signals. Our approach significantly improves the distance classification and consequently also the contact tracing accuracy. We are able to achieve good results with our approach in everyday public transport scenarios. However, in our implementation based on IEEE 802.11 probe requests, 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.