The spread of COVID-19 has been taken on pandemic magnitudes and has already spread over 200 countries in a few months. In this time of emergency of COVID-19, especially when there is still a need to follow the precautions and developed vaccines are not available to all the developing countries in the first phase of vaccine distribution, the virus is spreading rapidly through direct and indirect contacts. The World Health Organization (WHO) provides the standard recommendations on preventing the spread of COVID-19 and the importance of face masks for protection from the virus. The excessive use of manual disinfection systems has also become a source of infection. That is why this research aims to design and develop a low-cost, rapid, scalable, and effective virus spread control and screening system to minimize the chances and risk of spread of COVID-19. We proposed an IoT-based Smart Screening and Disinfection Walkthrough Gate (SSDWG) for all public places entrance. The SSDWG is designed to do rapid screening, including temperature measuring using a contact-free sensor and storing the record of the suspected individual for further control and monitoring. Our proposed IoT-based screening system also implemented real-time deep learning models for face mask detection and classification. This module classified individuals who wear the face mask properly, improperly, and without a face mask using VGG-16, MobileNetV2, Inception v3, ResNet-50, and CNN using a transfer learning approach. We achieved the highest accuracy of 99.81% while using VGG-16 and the second highest accuracy of 99.6% using MobileNetV2 in the mask detection and classification module. We also implemented classification to classify the types of face masks worn by the individuals, either N-95 or surgical masks. We also compared the results of our proposed system with state-of-the-art methods, and we highly suggested that our system could be used to prevent the spread of local transmission and reduce the chances of human carriers of COVID-19.

Healthcare facilities in modern age are key challenge especially in developing countries where remote areas face lack of high-quality hospitals and medical experts. As artificial intelligence has revolutionized various fields of life, health has also benefited from it. The existing architecture of store-and-forward method of conventional telemedicine is facing some problems, some of which are the need for a local health center with dedicated staff, need for medical equipment to prepare patient reports, time constraint of 24–48 hours in receiving diagnosis and medication details from a medical expert in a main hospital, cost of local health centers, and need for Wi-Fi connection. In this paper, we introduce a novel and intelligent healthcare system that is based on modern technologies like Internet of things (IoT) and machine learning. This system is intelligent enough to sense and process a patient’s data through a medical decision support system. This system is low-cost solution for the people of remote areas; they can use it to find out whether they are suffering from a serious health issue and cure it accordingly by contacting near hospitals. The results of the experiments also show that the proposed system is efficient and intelligent enough to provide health facilities. The results presented in this paper are the proof of the concept.

While accurate and complete modeling of the Internet topology at the Autonomous System (AS) level is critical for future protocol design, performance evaluation, simulation and analysis, still it remains a challenge to construct its accurate representation. In this paper, we collect BGP route announcements of ASes from Looking glass (LG) servers. By querying LG servers, we build an AS topology estimate of around 116 K AS links, from which we discover 11 K new AS links and 686 new ASes. We conclude that collecting BGP traces from LG servers can help enhance the current view of the AS topology from the BGP collector projects (e.g., RouteViews).

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The IRR is a set of globally distributed databases with which ASes can register their routing and address-related information. It is often believed that the quality of the IRR data is not reliable since there are few economic incentives for ASes to register and update their routing information in a timely manner. To validate these negative beliefs, we carry out a comprehensive analysis of (IP prefix, its origin AS) pairs in BGP against the corresponding information registered with the IRR, and vice versa. Considering the IRR and BGP practices, we propose a methodology to match the (IP prefix, origin AS) pairs between those two datasets. We observe that the practice of registering IP prefixes and origin ASes with the IRR is prevalent, though the quality of the IRR data varies substantially depending on routing registries, regional Internet registries (to which ASes belong), and AS types. The IRR can help improve the security level of BGP routing by making BGP routers selectively rely on the corresponding IRR data considering these observations.