Personal Devices for Contact Tracing: Smartphones and Wearables to Fight Covid-19

TL;DR

This paper reviews digital contact tracing methods using smartphones and wearables for Covid-19, analyzing their components, networking approaches, and proximity sensing performance through experiments.

Contribution

It provides a comprehensive review of contact tracing applications focusing on device components, networking strategies, and a comparative analysis of proximity sensing on smartphones and wearables.

Findings

Decentralized approaches enhance user privacy.

Wearables show comparable proximity detection accuracy to smartphones.

Experiments highlight differences in sensing performance between devices.

Abstract

Digital contact tracing has emerged as a viable tool supplementing manual contact tracing. To date, more than 100 contact tracing applications have been published to slow down the spread of highly contagious Covid-19. Despite subtle variabilities among these applications, all of them achieve contact tracing by manipulating the following three components: a) use a personal device to identify the user while designing a secure protocol to anonymize the user's identity; b) leverage networking technologies to analyze and store the data; c) exploit rich sensing features on the user device to detect the interaction among users and thus estimate the exposure risk. This paper reviews the current digital contact tracing based on these three components. We focus on two personal devices that are intimate to the user: smartphones and wearables. We discuss the centralized and decentralized networking approaches that use to facilitate the data flow. Lastly, we investigate the sensing feature available on smartphones and wearables to detect the proximity between any two users and present experiments comparing the proximity sensing performance between these two personal devices.