TY - JOUR
T1 - Infectious Probability Analysis on COVID-19 Spreading With Wireless Edge Networks
AU - Li, Xuran
AU - Guo, Shuaishuai
AU - Dai, Hong Ning
AU - Li, Dengwang
N1 - Funding information:
This work was supported in part by the National Natural Science Foundation of China under Grant 62171262 and Grant 61971271, in part by the Shandong Provincial Natural Science Foundation under Grant ZR2021YQ47, in part by the Major Scientific and Technological Innovation Project of Shandong Province under Grant 2020CXGC010109, in part by the Tashan Young Scholar under Grant tsqn201909043, in part by the Jinan City-School Integration Development Strategy Project under Grant JNSX2021023, in part by the Shandong Province Major Technological Innovation Project under Grant 2022CXGC010502, and in part by the State Key Laboratory of Synthetical Automation for Process Industries under Grant 2020-KF-21-06. (Corresponding author: Shuaishuai Guo.)
Publisher Copyright:
© 2022 IEEE.
PY - 2022/11
Y1 - 2022/11
N2 - The emergence of infectious disease COVID-19 has challenged and changed the world in an unprecedented manner. The integration of wireless networks with edge computing (namely wireless edge networks) brings opportunities to address this crisis. In this paper, we aim to investigate the prediction of the infectious probability and propose precautionary measures against COVID-19 with the assistance of wireless edge networks. Due to the availability of the recorded detention time and the density of individuals within a wireless edge network, we propose a stochastic geometry-based method to analyze the infectious probability of individuals. The proposed method can well keep the privacy of individuals in the system since it does not require to know the location or trajectory of each individual. Moreover, we also consider three types of mobility models and the static model of individuals. Numerical results show that analytical results well match with simulation results, thereby validating the accuracy of the proposed model. Moreover, numerical results also offer many insightful implications. Thereafter, we also offer a number of countermeasures against the spread of COVID-19 based on wireless edge networks. This study lays the foundation toward predicting the infectious risk in realistic environment and points out directions in mitigating the spread of infectious diseases with the aid of wireless edge networks.
AB - The emergence of infectious disease COVID-19 has challenged and changed the world in an unprecedented manner. The integration of wireless networks with edge computing (namely wireless edge networks) brings opportunities to address this crisis. In this paper, we aim to investigate the prediction of the infectious probability and propose precautionary measures against COVID-19 with the assistance of wireless edge networks. Due to the availability of the recorded detention time and the density of individuals within a wireless edge network, we propose a stochastic geometry-based method to analyze the infectious probability of individuals. The proposed method can well keep the privacy of individuals in the system since it does not require to know the location or trajectory of each individual. Moreover, we also consider three types of mobility models and the static model of individuals. Numerical results show that analytical results well match with simulation results, thereby validating the accuracy of the proposed model. Moreover, numerical results also offer many insightful implications. Thereafter, we also offer a number of countermeasures against the spread of COVID-19 based on wireless edge networks. This study lays the foundation toward predicting the infectious risk in realistic environment and points out directions in mitigating the spread of infectious diseases with the aid of wireless edge networks.
KW - Infectious probability analysis
KW - mobility models
KW - stochastic geometry
KW - wireless edge networks
UR - http://www.scopus.com/inward/record.url?scp=85139830680&partnerID=8YFLogxK
U2 - 10.1109/JSAC.2022.3211534
DO - 10.1109/JSAC.2022.3211534
M3 - Journal article
AN - SCOPUS:85139830680
SN - 0733-8716
VL - 40
SP - 3239
EP - 3254
JO - IEEE Journal on Selected Areas in Communications
JF - IEEE Journal on Selected Areas in Communications
IS - 11
ER -