Vaccinated, What Next? An Efficient Contact and Social Distance Tracing Based on Heterogeneous Telco Data

The demand for safety-boosting systems is always increasing, especially to limit the rapid spread of COVID-19. Real-time social distance preservation is an essential application toward containing the pandemic outbreak. Few systems have been proposed which require infrastructure setup and high-end phones. Therefore, they have limited ubiquitous adoption. Cellular technology enjoys widespread availability and their support by commodity cellphones, which suggest leveraging it for social distance tracking. However, users sharing the same environment may be connected to different telecom providers of different network configurations. Traditional cellular-based localization systems usually build a separate model for each provider, leading to a drop in social distance performance. In this article, we propose CellTrace , a deep learning-based social distance preserving system. Specifically, CellTrace finds a cross-provider representation using a deep learning version of canonical correlation analysis. Different providers' data are highly correlated in this representation and used to train a localization model for estimating the social distances. In addition, CellTrace incorporates different modules that improve the deep model's generalization against overtraining and noise. We have implemented and evaluated CellTrace in two different environments with a side-by-side comparison with the state-of-the-art cellular localization and contact tracing techniques. The results show that CellTrace can accurately localize users and estimate the contact occurrence, regardless of the connected providers, with a submeter median error and 97% accuracy, respectively. In addition, we show that CellTrace has robust performance in various challenging scenarios.