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Cellular-Assisted, Deep Learning Based COVID-19 Contact Tracing

Author(s): Yi, Fan; Xie, Yaxiong; Jamieson, Kyle

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Abstract: The Coronavirus disease (COVID-19) pandemic has caused social and economic crisis to the globe. Contact tracing is a proven effective way of containing the spread of COVID-19. In this paper, we propose CAPER, a Cellular-Assisted deeP lEaRning based COVID-19 contact tracing system based on cellular network channel state information (CSI) measurements. CAPER leverages a deep neural network based feature extractor to map cellular CSI to a neural network feature space, within which the Euclidean distance between points strongly correlates with the proximity of devices. By doing so, we maintain user privacy by ensuring that CAPER never propagates one client's CSI data to its server or to other clients. We implement a CAPER prototype using a software defined radio platform, and evaluate its performance in a variety of real-world situations including indoor and outdoor scenarios, crowded and sparse environments, and with differing data traffic patterns and cellular configurations in common use. Microbenchmarks show that our neural network model runs in 12.1 microseconds on the OnePlus 8 smartphone. End-to-end results demonstrate that CAPER achieves an overall accuracy of 93.39%, outperforming the accuracy of BLE based approach by 14.96%, in determining whether two devices are within six feet or not, and only misses 1.21% of close contacts. CAPER is also robust to environment dynamics, maintaining an accuracy of 92.35% after running for ten days.
Publication Date: 7-Sep-2022
Citation: Yi, Fan, Xie, Yaxiong, and Jamieson, Kyle. "Cellular-Assisted, Deep Learning Based COVID-19 Contact Tracing." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 6, no. 3 (2022): 1-27. doi:10.1145/3550332
DOI: 10.1145/3550332
EISSN: 2474-9567
Pages: 1 - 27
Language: en
Type of Material: Journal Article
Journal/Proceeding Title: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Version: Final published version. This is an open access article.

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