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 pre-cautionary 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.

Cloudlets are small self maintained clouds, with hotspot like deployment, to enhance the computational capabilities of the mobile devices. The limited resources of cloudlets can become heavily loaded during peak utilization. Consequently, per user available computational capacity decreases and at times mobile devices find no execution time benefit for using the cloudlet. Researchers have proposed augmenting the cloudlet resources using mobile devices; however, the proposed approaches do not consider the offered service to load ratio while using mobile device resources. In this paper, we propose easy to implement Mobile Device based Cloudlet Resource Enhancement (MobiCoRE) while ensuring that: (i) mobile device always have time benefit for its tasks submitted to the cloudlet and (ii) cloudlet induced mobile device load is a fraction of its own service requirement from the cloudlet. We map MobiCoRE on M/M/c/K queue and model the system using birth death markov chain. Given the arrival rate of λ, c cpu cores in cloudlet, maximum tasks in the cloudlet to be K and P0= f(λ, c, K, μ) be probability of having no user in cloudlet, we derive the condition 1/(P0) = (λK)/(cK-cdμK) 1000 for optimal average service time 1/μ of cloudlet such that the mobile applications have maximum benefit for using cloudlet services. We show that the optimal average service time is independent of the applications service requirement. Evaluation shows that MobiCoRE can accommodate up to 50 percent extra users when operating at optimal service time and sharing mobile resources for remaining task, compared to completing the entire user applications in cloudlet. Similarly, up to 47 percent time benefit can be achieved for mobile devices by sharing only 16 percent computational resources with the cloudlet.