Classical blockchain cryptographic primitives are susceptible to quantum computing technology due to its unprecedented growth because it has the potential to make current blockchain encryption techniques obsolete against quantum attacks. Shor algorithm can perform transactional hijacking and forge digital signatures to impersonate blockchain users. On the other hand, Grover algorithm can perform 51% attack and can accelerate brute force attack by detecting hash collisions to replace the blocks of a blockchain. With the advent of this threat, in attempt to mitigate the effects of quantum attacks on blockchain technology, the concept of post-quantum cryptography (PQC) or quantum resistant cryptography (QRC) has emerged. PQC involves cryptographic algorithms or cryptosystems that are robust against quantum attacks and are also applicable in blockchain for post quantum decentralization. In this article, we provide a brief overview of quantum threats to classical blockchain technology. We identify and discuss some important requirements for the implementation of PQC. Moreover, we also identify and explain notable unresolved challenges in implementing PQC algorithms. We also determine critical research questions for each identified challenge that are imperative to confer and requires discussion and interpretation by the researchers of this field.

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.