Proximity Inference with Wifi-Colocation during the COVID-19 Pandemic

TL;DR

This paper introduces a WiFi-based proximity inference method for digital contact tracing that is adaptable to various environments and shows promising preliminary results for COVID-19 exposure notification.

Contribution

It presents a novel WiFi colocation approach for contact tracing that is resilient to different scenarios by enabling devices to act as hotspots when access points are unavailable.

Findings

Feasibility of WiFi-based proximity detection demonstrated

Effective in both urban and rural environments

Potential to enhance existing contact tracing systems

Abstract

In this work we propose a WiFi colocation methodology for digital contact tracing. The approach works by having a device scan and store nearby access point information to perform proximity inference. We make our approach resilient to different practical scenarios by configuring a device to turn into a hotspot if access points are unavailable, which makes the approach feasible in both dense urban areas and sparse rural places. We compare various shortcomings and advantages of this work over other conventional ways of doing digital contact tracing. Preliminary results indicate the feasibility of our approach for determining proximity between users, which is relevant for improving existing digital contact tracing and exposure notification implementations.