COVID-19 Tracer: Towards Low-cost Passive Close-contacts Searching

Autor: Peihao Li, Qiang Niu, Pengpeng Chen, Xu Yang, Mingzhi Pang, Yuqing Yin
Rok vydání: 2020
Předmět:
Zdroj: 2020 International Conference on Internet of Things and Intelligent Applications (ITIA).
DOI: 10.1109/itia50152.2020.9312302
Popis: COVID-19 is spreading rapidly around the world, which is the enemy faced by all mankind. Since COVID-19 is mainly spread through close personal contact, searching closecontacts is the key to controlling the spread of this virus. This paper designs a new low-cost passive searching system of COVID19 patients’ close-contacts using ubiquitous WiFi probe requests, called COVID-19 Tracer. In COVID-19 Tracer, we propose a novel rang-free judgment algorithm for location similarity, which includes two judgment indicators: location similarity coefficient and close contact distance. Finally, we conduct extensive experiments in a school office building. The experimental results show that our system has good performance, and our system’s accuracy in judging close-contacts is more than 98%.
Databáze: OpenAIRE