A Privacy-Preserving Mobile and Fog Computing Framework to Trace and Prevent COVID-19 Community Transmission

Abstract

To slow down the spread of COVID-19, governments worldwide are trying to identify infected people and contain the virus by enforcing isolation and quarantine. However, it is difficult to trace people who came into contact with an infected person, which causes widespread community transmission and mass infection. To address this problem, we develop an e-government Privacy-Preserving Mobile and Fog computing framework entitled PPMF that can trace infected and suspected cases nationwide. We use personal mobile devices with contact tracing app and two types of stationary fog nodes, named Automatic Risk Checkers (ARC) and Suspected User Data Uploader Node (SUDUN), to trace community transmission alongside maintaining user data privacy. Each user's mobile device receives a Unique Encrypted Reference Code (UERC) when registering on the central application. The mobile device and the central application both generate Rotational Unique Encrypted Reference Code (RUERC), which broadcasted using the Bluetooth Low Energy (BLE) technology. The ARCs are placed at the entry points of buildings, which can immediately detect if there are positive or suspected cases nearby. If any confirmed case is found, the ARCs broadcast precautionary messages to nearby people without revealing the identity of the infected person. The SUDUNs are placed at the health centers that report test results to the central cloud application. The reported data is later used to map between infected and suspected cases. Therefore, using our proposed PPMF framework, governments can let organizations continue their economic activities without complete lockdown.

IoT‐fog‐cloud based architecture for smart systems: Prototypes of autism and COVID‐19 monitoring systems

“…They have taken account of the heterogeneity of the computing capabilities in addition to the OS and peer‐to‐peer communication standards. None of these proposals did focus on the heterogeneity of fog/cloud providers in terms of the price and QoS strategies. Scalability and elasticity: The fog and cloud nodes should be flexible to be scaled up or down to be elastically operational such as References 2,14,17,19‐23. Whaiduzzaman et al 17 developed an e‐government privacy preserving mobile and fog computing framework for monitoring the infected and suspected cases.…”

“…Thus, it is useful for adding scalability and availability to the message queue service. Monitoring and KPIs Dashboard: Providing a real‐time dashboard of personalized KPIs for management and visualization via an IoT and/or end‐user device. The authors in References 2,14‐21 dealt with the real‐time monitoring and data analytics in the IoT‐aware fog/cloud environment. In this context, Debauche et al 15 developed a NodeJS web service for monitoring the battery level and sending alerts when the level is lower than 20%.…”

“…Whilst, Otoom et al 18 suggested an IoT‐based framework to monitor the COVID‐19 suspected infected cases in real time. Similarly, Whaiduzzaman et al 17 proposed a mobile and fog computing framework for monitoring the COVID‐19 infected cases. Although these works took monitoring service into account, none of them have offered a personalized KPI dashboard. Fog multitenancy: Both the fog and cloud systems should support the multiuser application deployment.…”

“…Data may be also stored in the cloud layer for batch analytics over the long term. Some of the proposed architectures did not meet the short‐term storage such as in References 2,17,18,23. Other existing works did not satisfy the long‐term storage such Reference 23.…”

“…Several works 2,14‐16 have introduced the fog computing as a sustainable solution to the challenges of today's IoT health systems. Since the end of 2019, several works 17,18 have attempted to combat the coronavirus disease 2019 (COVID‐19) pandemic. They have adopted cloud and fog capabilities, and proposed an IoT‐based framework for monitoring COVID‐19 cases.…”