The ongoing pandemic of Corona-Virus (COVID-19) induced by the coming forth category of SARS-CoV-2, has terrified the worldwide human health. Primarily, COVID-19 challenges can be categorized into (a) way of epidemic prevention and blocking transmission, (b) live monitoring of infected / suspected persons (c) FDA approved vaccine. Leading to said COVID-19 (a), (b) challenges, digit technologies such Artificial Intelligence, Big data analytics and Internet of Things (IoT), can play a vital role in epidemic prevention and blocking COVID-19 transmission. In this study, we have proposed a smart edge surveillance system that is effective in remote monitoring, advance warning and detection of a person's fever, heart beat rate, cardiac conditions and some of the radiological features to detect the infected (suspicious) person using wearable smart gadgets. The proposed framework provides a continually updated map/pattern of communication chain of COVID-19 infected persons that may span around in our national community. The health and societal impact of suggested research is to help public health authorities, researchers and clinicians contain and manage this disease through smart edge surveillance systems. The proposed model will help to detect and track the contagious person. Moreover, it will also keep the patient's data record for analysis and decision making using edge computing.
Sharing cloud resources between groups of users is a challenge. Cloud providers do not commonly support users in sharing their spare dedicated resources with others. In developing countries, it is often too expensive for people to acquire a virtual machine of their own. Users may, therefore, wish to manage costs and increase computational resource usage by sharing their instances with others. This paper presents a container based Cloud Resource Bartering (CRB) model for sharing user's computational resources through a social network. In our approach, we have integrated a Facebook account with the computational cloud to enable tenants to share their unused cloud resources with other users. The performance of the proposed prototype is evaluated under different workloads. Based on our experimental results we conclude that the proposed model is well suited for the creation of a low-cost social cloud in developing countries.
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