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Evaluation of the Bluetooth-based Proximity Estimation for Automatic Exposure Determination

Published

Author(s)

Kamran Sayrafian, Brian D. Cloteaux, Vladimir Marbukh, Christian Emiyah

Abstract

A commonly used methodology to estimate the proximity of two individuals in an automatic exposure notification system is using the signal strength of the Bluetooth signal from their mobile phones. However, there is an underlying error in this Bluetooth-based proximity detection that could result in wrong exposure decisions. A wrong decision in the exposure determination leads to two types of errors: false negative and false positive. False negative occurs when an exposed individual is incorrectly identified as not exposed. Similarly, a false positive occurs when a non-exposed individual is mistakenly identified as exposed. Both errors have costly implications; and can ultimately determine the effectiveness of Bluetooth-based automatic exposure notification in containment of pandemics such as COVID-19. In this paper, we present a platform that allows for the analysis of the system performance under various parameters. This platform enables us to gain a better understanding on how the underlying technology error propagates through the contact tracing system. Preliminary results show the considerable impact of the Bluetooth-based proximity estimation error on false exposure determination. Alternatively, using this platform, analysis could be performed to determine the acceptable accuracy level of a proximity detection mechanism in order to have a more effective contact tracing solution.
Conference Dates
December 7-11, 2021
Conference Location
Madrid, ES
Conference Title
The 2021 IEEE Global Communications Conference (GLOBECOM)

Keywords

Proximity Detection, Bluetooth, Exposure Notification, COVID-19, Contact Tracing

Citation

Sayrafian, K. , Cloteaux, B. , Marbukh, V. and Emiyah, C. (2022), Evaluation of the Bluetooth-based Proximity Estimation for Automatic Exposure Determination, The 2021 IEEE Global Communications Conference (GLOBECOM) , Madrid, ES, [online], https://doi.org/10.1109/CCNC49033.2022.9700648, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=932633 (Accessed July 19, 2024)

Issues

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Created February 10, 2022, Updated November 29, 2022