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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.

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With a rapid increase in population, many problems arise in relation to waste dumps. These emits hazardous gases, which have negative effects on human health. The main issue is the domestic solid waste collection, management, and classification. According to studies, in America, nearly 75% of waste can be recycled, but there is a lack of a proper real-time waste-segregating mechanism, due to which only 30% of waste is being recycled at present. To maintain a clean and green environment, we need a smart waste management and classification system. To tackle the above-highlighted issue, we propose a real-time smart waste management and classification mechanism using a cutting-edge approach (SWMACM-CA). It uses the Internet of Things (IoT), deep learning (DL), and cutting-edge techniques to classify and segregate waste items in a dump area. Moreover, we propose a waste grid segmentation mechanism, which maps the pile at the waste yard into grid-like segments. A camera captures the waste yard image and sends it to an edge node to create a waste grid. The grid cell image segments act as a test image for trained deep learning, which can make a particular waste item prediction. The deep-learning algorithm used for this specific project is Visual Geometry Group with 16 layers (VGG16). The model is trained on a cloud server deployed at the edge node to minimize overall latency. By adopting hybrid and decentralized computing models, we can reduce the delay factor and efficiently use computational resources. The overall accuracy of the trained algorithm is over 90%, which is quite effective. Therefore, our proposed (SWMACM-CA) system provides more accurate results than existing state-of-the-art solutions, which is the core objective of this work.

Vehicular delay tolerant network (VDTN) is a widely used communication standard for the scenarios where no end to end path is available between nodes. Data is sent from one node to another node using routing protocols of VDTN. These routing protocols use different decision metrics. Based on these metrics, it is chosen whether to send data to connected node or find another suitable candidate. These metrices are Time to live (TTL), geographical information, destination utility, relay utility, meeting prediction, total and remaining buffer size and many other. Different routing protocols use a different combination of metrics. In this paper, a metric called "estimation-time" is introduced. The "estimation-time" is assessed at the encounter of two nodes. Nodes may decide based on that whether to send data or not. This metric can be used in routing decisions. The simulations results are above 88% which proves "estimationtime" metric is calculated correctly.