An Efficient Framework for Securing the Smart City Communication Networks.

Recently, smart cities have increasingly been experiencing an evolution to improve the lifestyle of citizens and society. These emerge from the innovation of information and communication technologies (ICT) which are able to create a new economic and social opportunities. However, there are several challenges regarding our security and expectation of privacy. People are already involved and interconnected by using smart phones and other appliances. In many cities, smart energy meters, smart devices, and security appliances have recently been standardized. Full connectivity between public venues, homes, cares, and some other social systems are on their way to be applied, which are known as Internet of Things. In this paper, we aim to enhance the performance of security in smart city communication networks by using a new framework and scheme that provide an authentication and high confidentiality of data. The smart city system can achieve mutual authentication and establish the shared session key schemes between smart meters and the control center in order to secure a two-way communication channel. In our extensive simulation, we investigated and evaluated the security performance of the smart city communication network with and without our proposed scheme in terms of throughput, latency, load, and traffic received packet per seconds. Furthermore, we implemented and applied a man-in-the-middle (MITM) attack and network intrusion detection system (NIDS) in our proposed technique to validate and measure the security requirements maintaining the constrained resources.

Augmented Reality (AR) and Cyber-Security for Smart Cities-A Systematic Literature Review.

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.

Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques.

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.

An AUV-Aided Cross-Layer Mobile Data Gathering Protocol for Underwater Sensor Networks.

Underwater sensor networks (UWSNs) have recently attracted much attention due to their ability to discover and monitor the aquatic environment. However, acoustic communication has posed some significant challenges, such as high propagation delay, low available bandwidth, and high bit error rate. Therefore, proposing a cross-layer protocol is of high importance to the field to integrate different communication functionalities (i.e, an interaction between data link layer and network layer) to interact in a more reliable and flexible manner to overcome the consequences of applying acoustic signals. In this paper, a novel Cross-Layer Mobile Data gathering (CLMD) scheme for Underwater Sensor Networks (UWSNs) is presented to improve the performance by providing the interaction between the MAC and routing layers. In CLMD, an Autonomous Underwater Vehicle (AUV) is used to periodically visit a group of clusters which are responsible for data collection from members. The communications are managed by using a distributed cross-layer solution to enhance network performance in terms of packet delivery and energy saving. The cluster heads are replaced with other candidate members at the end of each operational phase to prolong the network lifetime. The effectiveness of CLMD is verified through an extensive simulation study which reveals the performance improvement in the energy-saving, network lifetime, and packet delivery ratio with varying number of nodes. The effects of MAC protocols are also studied by studying the network performance under various MAC protocols in terms of packet delivery ratio, goodput, and energy consumption with varying density of nodes.