Risk area alert through heterogeneous mobile networks: a new approach to fight COVID-19 and beyond

Sun, Y. , Xue, Q., Zhang, L. , Mohjazi, L. , Cao, B. and Imran, M. A. (2022) Risk area alert through heterogeneous mobile networks: a new approach to fight COVID-19 and beyond. IEEE Network, 36(6), pp. 20-26. (doi: 10.1109/MNET.003.2100205)

Abstract

COVID-19 has now been sweeping the whole world, and fundamentally affecting our daily life. An effective mechanism to further fight against COVID-19 and prevent the spread of this pandemic is to alert people when they are in the vicinity of areas with a high infection risk, yielding them to adjust their routes and consequently, leave these areas. Inspired by the fact that mobile communication networks are capable of precise positioning, data processing and information broadcasting, as well as are available for almost every person, in this paper, we propose a mobile network assisted Risk arEa ALerting scheme, named REAL, which exploits heterogeneous mobile networks to alert users who are in/near to the areas with high risks of COVID- 19 infection. Specifically, in REAL scheme, all base stations (BSs) periodically estimate their serving users' locations, which are then analyzed by macro BSs (MBSs) to identify risk areas. Next, each MBS transmits the information about risk areas to small BSs (SBSs), which in their turn adjust the beamforming direction to cover these areas and send alerts to users located therein. Simulation results validate the effectiveness of the proposed REAL scheme. In addition, some key challenges associated with implementing REAL are discussed at the end.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zhang, Professor Lei and Imran, Professor Muhammad and Sun, Dr Yao and Mohjazi, Dr Lina
• Authors: Sun, Y., Xue, Q., Zhang, L., Mohjazi, L., Cao, B., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity
• Journal Name: IEEE Network
• Publisher: IEEE
• ISSN: 0890-8044
• ISSN (Online): 1558-156X
• Published Online: 08 August 2022
• Copyright Holders: Copyright © 2022 IEEE
• First Published: First published in IEEE Network 36(6): 20-26
• Publisher Policy: Reproduced in accordance with the publisher copyright policy