论文标题
与WiFi-Cologation的距离推断在19号大流行期间
Proximity Inference with Wifi-Colocation during the COVID-19 Pandemic
论文作者
论文摘要
在这项工作中，我们提出了用于数字接触跟踪的WiFi托管方法。该方法可以通过设备扫描并存储附近的访问点信息来执行邻近推理来起作用。如果不可用的接入点，我们通过配置设备将设备变成热点，从而使我们的方法能够韧性，这使得该方法在茂密的城市地区和稀疏的农村地方都可可行。我们比较了这项工作的各种缺点和优势，而不是其他传统的数字接触跟踪方式。初步结果表明我们的方法可以确定用户之间接近性的可行性，这与改善现有的数字接触跟踪和曝光通知实现相关。
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.
