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The Flying Ad hoc Network (FANET) is a special type of mobile ad hoc network (MANET) that provides communications among Unmanned Aerial Vehicles (UAVs). These UAVs can reduce human intervention to a great extent by giving numerous applications under different domains such as transportation, military, healthcare, traffic monitoring, surveillance, etc. In FANET, communication is relatively challenging due to its complex infrastructure, unspecified architecture and rapid mobility of nodes. The work embodied in this paper is focused on traffic surveillance of highways using UAVs. UAVs can help to reduce the number of accidents by sharing real-time as well as the accurate status of highways among vehicles and the control station. On the other hand, they can also be used to track specific vehicles on the road. In FANET, both routing protocols and mobility models play a crucial role in the process of information exchange. In this paper, the comparison and performance evaluation of two well-known reactive routing protocols viz. Ad hoc On-demand Distance Vector (AODV) and Dynamic Source Routing (DSR) have been carried out using highway mobility model for traffic surveillance in FANET environment. Implementation of both protocols has been tested on several traffic patterns, mobility and varying network loads. Both AODV and DSR enable significant performance variations; however, they share on-demand behavior. Packet delivery fraction, average end-to-end delay, normalized routing load, packet loss, routing overhead and throughput are used to analyze the performance of both protocols. Based on experimental analysis using NS-2 under constant bit rate (CBR) and TCP traffic sources, it can be stated that AODV outperforms DSR in almost every aspect.

Artificial Intelligence (AI) has been applied successfully in many real-life domains for solving complex problems. With the invention of Machine Learning (ML) paradigms, it becomes convenient for researchers to predict the outcome based on past data. Nowadays, ML is acting as the biggest weapon against the COVID-19 pandemic by detecting symptomatic cases at an early stage and warning people about its futuristic effects. It is observed that COVID-19 has blown out globally so much in a short period because of the shortage of testing facilities and delays in test reports. To address this challenge, AI can be effectively applied to produce fast as well as cost-effective solutions. Plenty of researchers come up with AI-based solutions for preliminary diagnosis using chest CT Images, respiratory sound analysis, voice analysis of symptomatic persons with asymptomatic ones, and so forth. Some AI-based applications claim good accuracy in predicting the chances of being COVID-19-positive. Within a short period, plenty of research work is published regarding the identification of COVID-19. This paper has carefully examined and presented a comprehensive survey of more than 110 papers that came from various reputed sources, that is, Springer, IEEE, Elsevier, MDPI, arXiv, and medRxiv. Most of the papers selected for this survey presented candid work to detect and classify COVID-19, using deep-learning-based models from chest X-Rays and CT scan images. We hope that this survey covers most of the work and provides insights to the research community in proposing efficient as well as accurate solutions for fighting the pandemic.

Mobile ad-hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any stand-alone infrastructure or centralized administration. Mobile ad-hoc network have the attribute such as wireless connection, continuously changing topology, distributed operation and ease of deployment. In this paper we have investigated the performance of two MANET routing protocol (Proactive and Reactive) by using Freeway and Random Waypoint mobility model for mobility of nodes using TCP traffic. Freeway Mobility model has been generated by IMPORTANT (Impact of Mobility Patterns on Routing in Ad-hoc NeTwork) tool, whereas Random Waypoint by inbuilt setdest tool in NS2. A detailed simulation has been carried out in NS2 with TCP traffic sources and AODV as reactive and DSDV as proactive routing protocols. The metrics used for performance analysis are Packet Delivery Fraction, Average end-to-end Delay, Packet Loss, Routing Overhead, Normalized Routing Load and throughput. It has been observed that (proactive routing protocol) DSDV performance is better than (reactive routing protocol) AODV but at the cost of higher average end-end delay in both mobility models. Both routing protocols give optimized result in Random Waypoint mobility model as compared to Freeway Mobility Model.

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