DeepAI AI Chat
Log In Sign Up

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

by   Mikhail Dmitrienko, et al.

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.


page 1

page 2

page 3

page 4


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

Digital contact tracing has emerged as a viable tool supplementing manua...

Hansel and Gretel and the Virus: Privacy Conscious Contact Tracing

Digital contact tracing has been proposed to support the health authorit...

Proximity Sensing for Contact Tracing

The TC4TL (Too Close For Too Long) challenge is aimed towards designing ...

Digital Contact Tracing Service: An improved decentralized design for privacy and effectiveness

We propose a decentralized digital contact tracing service that preserve...

Risk Estimation of SARS-CoV-2 Transmission from Bluetooth Low Energy Measurements

Digital contact tracing approaches based on Bluetooth low energy (BLE) h...

Improving Proximity Estimation for Contact Tracing using a Multi-channel Approach

Due to the COVID 19 pandemic, smartphone-based proximity tracing systems...

PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols

Many proximity-based tracing (PCT) protocols have been proposed and depl...