Software-defined networking (SDN) plays a crucial role in the enterprise and wide-area networking. The increasing demand for strict service-level agreement applications on the Internet requires networks to be scalable and resilient in the face of link and switch failure. However, there is a lack of systematic reviews on SDN data plane failure recovery techniques. This review article assesses SDN current state-of-the-art link and switches failure recovery solutions. We cover the root causes of failures in the traditional core network and their detection and classify the current failure recovery techniques for SDN into two categories: traditional and artificial intelligence (AI) approaches. AI-based techniques enable efficient failure recovery and enhance the quality of service. We also consider performance measure metrics to evaluate and determine the limitations of existing solutions. This study reviews 188 papers from 2010 to 2021, selecting 70 articles that are highly relevant to our work. All articles are written in English. Our research aims to collect a large amount of evidence that will assist the industry and academic researchers in net- working to address current research gaps in failure recovery solutions for the SDN data plane.

In this work, we have highlighted the recent developments on Fake news detection.

Internet of Things (IoT) vision has astoundingly transcended the environmental sensing with integrated computing systems and smart devices, providing seamless connectivity among humans, machines and their environment to cooperate for convenience and economical benefits. Apart from all the tremendous benefits of IoT, this paradigm still suffers from challenges of security and privacy vulnerabilities and demands a secure system for effective utilization of services in real world IoT scenarios relaying on which the IoT consumers expect secure and trustworthy communications. Trust Management (TM) being a crucial aspect of security, plays a vital role in ensuring the exchange of information in a secure manner and maintaining the reliability of a system by measuring the degree of trust on IoT devices, reducing the uncertainties and risks involved in the systems. Centralized TM systems for IoT have a potential risk of failure and attacks and decentralized TM systems are not completely decentralized, requiring the assistance of central servers for the completion of final calculation and maintenance of trust credits. Thus, in recent years, Blockchain technology has been utilized for developing security innovations in Trust management field for different classes of IoT applications. It can provide tamper proof data by enabling a more reliable trust information and integrity verification, ultimately enhancing its availability and privacy during storage and sharing. This paper provides a comprehensive survey that aims at analyzing and assessing Blockchain based Decentralized Trust Management Systems (BCDTMS) for IoT. In this paper, our contributions are twofold; first we provide a comprehensive comparative analysis of state-of-the-art BCDTMS devised for different IoT classes including IoMT, IoV, IIoT and SIoT. To make it an extensive study, we perform a detailed Assessment of existing BCDTMS in the literature (for last three years 2018-2020) on the basis of Blockchain and trust based aspects. Secondly, we present requirements and challenges in the context of using blockchain for TM in IoT.

Coronavirus (COVID-19) is an ecumenical pandemic that has affected the whole world drastically by raising a global calamitous situation. Due to this pernicious disease, millions of people have lost their lives. The scientists are still far from knowing how to tackle the coronavirus due to its multiple mutations found around the globe. Standard testing technique called Polymerase Chain Reaction (PCR) for the clinical diagnosis of COVID-19 is expensive and time consuming. However, to assist specialists and radiologists in COVID-19 detection and diagnosis, deep learning plays an important role. Many research efforts have been done that leverage deep learning techniques and technologies for the identification or categorization of COVID-19 positive patients, and these techniques are proved to be a powerful tool that can automatically detect or diagnose COVID-19 cases. In this paper, we identify significant challenges regarding deep learning-based systems and techniques that use different medical imaging modalities, including Cough and Breadth, Chest X-ray, and Computer Tomography (CT) to combat COVID-19 outbreak. We also pinpoint important research questions for each category of challenges. The challenges highlighted in this paper will call an attention to the noticeable weaknesses and problems in the existing deep learning based COVID-19 detection systems and techniques. Moreover, the research questions for each challenge will guide the researchers to come up with novel solutions in COVID-19 detection.