Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.

Augmented Reality (AR) and cyber-security technologies have existed for several decades, but their growth and progress in recent years have increased exponentially. The areas of application for these technologies are clearly heterogeneous, most especially in purchase and sales, production, tourism, education, as well as social interaction (games, entertainment, communication). Essentially, these technologies are recognized worldwide as some of the pillars of the new industrial revolution envisaged by the industry 4.0 international program, and are some of the leading technologies of the 21st century. The ability to provide users with required information about processes or procedures directly into the virtual environment is archetypally the fundamental factor in considering AR as an effective tool for different fields. However, the advancement in ICT has also brought about a variety of cybersecurity challenges, with a depth of evidence anticipating policy, architectural, design, and technical solutions in this very domain. The specific applications of AR and cybersecurity technologies have been described in detail in a variety of papers, which demonstrate their potential in diverse fields. In the context of smart cities, however, there is a dearth of sources describing their varied uses. Notably, a scholarly paper that consolidates research on AR and cybersecurity application in this context is markedly lacking. Therefore, this systematic review was designed to identify, describe, and synthesize research findings on the application of AR and cybersecurity for smart cities. The review study involves filtering information of their application in this setting from three key databases to answer the predefined research question. The keynote part of this paper provides an in-depth review of some of the most recent AR and cybersecurity applications for smart cities, emphasizing potential benefits, limitations, as well as open issues which could represent new challenges for the future. The main finding that we found is that there are five main categories of these applications for smart cities, which can be classified according to the main articles, such as tourism, monitoring, system management, education, and mobility. Compared with the general literature on smart cities, tourism, monitoring, and maintenance AR applications appear to attract more scholarly attention.

The Medium Access Control (MAC) layer protocol is the most important part of any network, and is considered to be a fundamental protocol that aids in enhancing the performance of networks and communications. However, the MAC protocol’s design for underwater sensor networks (UWSNs) has introduced various challenges. This is due to long underwater acoustic propagation delay, high mobility, low available bandwidth, and high error probability. These unique acoustic channel characteristics make contention-based MAC protocols significantly more expensive than other protocol contentions. Therefore, re-transmission and collisions should effectively be managed at the MAC layer to decrease the energy cost and to enhance the network’s throughput. Consequently, handshake-based and random access-based MAC protocols do not perform as efficiently as their achieved performance in terrestrial networks. To tackle this complicated problem, this paper surveys the current collision-free MAC protocols proposed in the literature for UWSNs. We first review the unique characteristic of underwater sensor networks and its negative impact on the MAC layer. It is then followed by a discussion about the problem definition, challenges, and features associated with the design of MAC protocols in UWANs. Afterwards, currently available collision-free MAC design strategies in UWSNs are classified and investigated. The advantages and disadvantages of each design strategy along with the recent advances are then presented. Finally, we present a qualitative comparison of these strategies and also discuss some possible future directions.

Employing contention-based medium access control (MAC) protocols in underwater sensor networks (UWSNs) is typically costly. This is due to the unique characteristics of underwater acoustic channels such as long propagation delay, limited bandwidth, and high bit error rate. As a consequence, the contention-based (handshaking and random access-based) MAC protocols do not perform as efficiently as expected. The collision-free approach is therefore considered to achieve a high performance by avoiding the collisions at MAC layer in order to improve the energy efficiency, throughput, and fairness. In this paper, we propose, inspired by graph colouring techniques, a novel energy-conserving and collision-free reservation-based MAC protocol, called GC-MAC, for UWSNs. GC-MAC employs a TDMA-like approach by assigning separate time-slots, colours, to every individual sensor node in every two-hop neighbourhood. Sensors with the same colours can thus transmit at the same time with no chance of collision. GCMAC is also able to address the near-far effect, spatial-temporal uncertainty, and hidden/exposed node problems, without requiring CDMA or power adjustment for collision avoidance. The network coverage and connectivity is then discussed to show the effectiveness of using cubes to cover a three-dimensional (3D) underwater environment. Our extensive performance study shows that GC-MAC performs well by avoiding collisions achieve better throughput and energy-efficiency performance compared to those of contentionbased protocols. There is also a significant improvement in terms of packet delivery ratio and fairness among the nodes under different operational conditions.

Cyber-attacks have become commonplace in the world of the Internet. The nature of cyber-attacks is gradually changing. Early cyber-attacks were usually conducted by curious personal hackers who used simple techniques to hack homepages and steal personal information. Lately, cyber attackers have started using sophisticated cyber-attack techniques that enable them to retrieve national confidential information beyond the theft of personal information or defacing websites. These sophisticated and advanced cyber-attacks can disrupt the critical infrastructures of a nation. Much research regarding cyber-attacks has been conducted; however, there has been a lack of research related to measuring cyber-attacks from the perspective of offensive cybersecurity. This motivated us to propose a methodology for quantifying cyber-attacks such that they are measurable rather than For this purpose, we identified each element of offensive cybersecurity used in cyber-attacks. We also investigated the extent to which the detailed techniques identified in the offensive cyber-security framework were used, by analyzing cyber-attacks. Based on these investigations, the complexity and intensity of cyber-attacks can be measured and quantified. We evaluated advanced persistent threats (APT) and fileless cyber-attacks that occurred between 2010 and 2020 based on the methodology we developed. Based on our research methodology, we expect that researchers will be able to measure future cyber-attacks.

Underwater Sensor Network (UWSN) has recently attracted a considerable attention due to its ability to discover and monitor the aquatic environment. However, its acoustic communication has posed several inherent characteristics such as high latency, low available bandwidth, and high bit error rate. These unique characteristics have made contention-based Medium Access Control (MAC) protocols inefficient for UWSNs. They are mostly expensive and are not as effective as they are in terrestrial networks. Through this principle, a contention-free MAC protocol is therefore considered to be more reliable and flexible to overcome the consequences of applying acoustic signals, and also to achieve a high performance (improving the energy efficiency and throughput across the network) by eliminating the chance of collision. In this paper, we propose a novel energy-conserving and collision-free Depth-based Layering MAC protocol (DLMAC) for UWSNs. DL-MAC is able to deal with the underwater MAC challenges such as the near-far effect, spatial-temporal uncertainty, and hidden/exposed terminal problems. It is able to efficiently schedule the transmission and receptions operations in each side by using the concept of layering and a distributed clustering algorithm. By using a TDMA-based principle, DL-MAC can assign separate time slots to every sensor node individually to access the medium without any possibility of collision. Our extensive simulation study shows that DL-MAC outperforms other protocols in terms of throughput, packet delivery ratio, energy consumption, and packets lost under varying traffic rates and numbers of nodes.