New Frameworks for Secure Image Communication in the Internet of Things (IoT)

The continuous expansion of technology, broadband connectivity and the wide range of new devices in the IoT cause serious concerns regarding privacy and security. In addition, in the IoT a key challenge is the storage and management of massive data streams. For example, there is always the demand for acceptable size with the highest quality possible for images to meet the rapidly increasing number of multimedia applications. The effort in this dissertation contributes to the resolution of concerns related to the security and compression functions in image communications in the Internet of Thing (IoT), due to the fast of evolution of IoT. This dissertation proposes frameworks for a secure digital camera in the IoT. The objectives of this dissertation are twofold. On the one hand, the proposed framework architecture offers a double-layer of protection: encryption and watermarking that will address all issues related to security, privacy, and digital rights management (DRM) by applying a hardware architecture of the state-of-the-art image compression technique Better Portable Graphics (BPG), which achieves high compression ratio with small size. On the other hand, the proposed framework of SBPG is integrated with the Digital Camera. Thus, the proposed framework of SBPG integrated with SDC is suitable for high performance imaging in the IoT, such as Intelligent Traffic Surveillance (ITS) and Telemedicine. Due to power consumption, which has become a major concern in any portable application, a low-power design of SBPG is proposed to achieve an energy- efficient SBPG design. As the visual quality of the watermarked and compressed images improves with larger values of PSNR, the results show that the proposed SBPG substantially increases the quality of the watermarked compressed images. Higher value of PSNR also shows how robust the algorithm is to different types of attack. From the results obtained for the energy- efficient SBPG design, it can be observed that the power consumption is substantially reduced, up to 19%

The medical and biochemical knowledge of health care professionals regarding the management of MERS-CoV: lessons from 2019 pilgrimage season in Al-Madinah, Saudi Arabia: A cross-sectional study

Background: Middle East respiratory syndrome coronavirus (MERS-CoV) epidemic was a serious healthcare concern not responding to conventional anti-viral therapies between 2012 and 2017 with high fatality rates. Saudi Arabia is still among the best world examples in combating both MERS-CoV and COVID-19 pandemics. Objectives: Investigating the medical and biochemical knowledge of healthcare professionals in Al-Madinah, Saudi Arabia on preventive, diagnostic and therapeutic measures against MERS-CoV epidemic. Subjects and methods: In 2019, this cross-sectional study included 416 healthcare personnel of which 402 participants answered the questions with a response rate of 96.7%. Specialties of participants were medical students (1.4%), physicians (64.4%), nurses (23.6%) and others (10.7%). Results: The vast majority of the investigated healthcare personnel gave the right answers. 96.7% of the participants answered that washing hands using water helps prevent MERS-CoV. 90.8% of the participants answered that wearing a clean non-sterile long-sleeved gown and gloves does helps prevent MERS-CoV infection. 94.7% of participants answered that using alcohol-based hand rub helps prevent MERS-CoV infection. 92.03% of the participants thought that wearing protective equipment does help preventing MERS-CoV infection. 86.1% answered that there is no vaccine available against MERS-CoV infection and 86.1% answered that taking vaccines is suitable for preventing MERS-CoV infection. 90.04% of the participants answered that MERS-CoV patients should be diagnosed using PCR and 84.3% thought that the highest levels of anti-CoV antibodies are in abattoir workers while 87.8% thought that isolation of suspected cases helps preventing MERS-CoV infection. Conclusion: The investigated healthcare workers had a satisfactory knowledge on the preventive and therapeutic measures and biochemical knowledge against MERS-CoV epidemic at mass gatherings as pilgrimage season

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Current Artificial Intelligence (AI) Techniques, Challenges, and Approaches in Controlling and Fighting COVID-19: A Review

SARS-CoV-2 (COVID-19) has been one of the worst global health crises in the 21st century. The currently available rollout vaccines are not 100% effective for COVID-19 due to the evolving nature of the virus. There is a real need for a concerted effort to fight the virus, and research from diverse fields must contribute. Artificial intelligence-based approaches have proven to be significantly effective in every branch of our daily lives, including healthcare and medical domains. During the early days of this pandemic, artificial intelligence (AI) was utilized in the fight against this virus outbreak and it has played a major role in containing the spread of the virus. It provided innovative opportunities to speed up the development of disease interventions. Several methods, models, AI-based devices, robotics, and technologies have been proposed and utilized for diverse tasks such as surveillance, spread prediction, peak time prediction, classification, hospitalization, healthcare management, heath system capacity, etc. This paper attempts to provide a quick, concise, and precise survey of the state-of-the-art AI-based techniques, technologies, and datasets used in fighting COVID-19. Several domains, including forecasting, surveillance, dynamic times series forecasting, spread prediction, genomics, compute vision, peak time prediction, the classification of medical imaging—including CT and X-ray and how they can be processed—and biological data (genome and protein sequences) have been investigated. An overview of the open-access computational resources and platforms is given and their useful tools are pointed out. The paper presents the potential research areas in AI and will thus encourage researchers to contribute to fighting against the virus and aid global health by slowing down the spread of the virus. This will be a significant contribution to help minimize the high death rate across the globe

Securing Session Initiation Protocol

The session initiation protocol (SIP) is widely used for multimedia communication as a signaling protocol for managing, establishing, maintaining, and terminating multimedia sessions among participants. However, SIP is exposed to a variety of security threats. To overcome the security flaws of SIP, it needs to support a number of security services: authentication, confidentiality, and integrity. Few solutions have been introduced in the literature to secure SIP, which can support these security services. Most of them are based on internet security standards and have many drawbacks. This work introduces a new protocol for securing SIP called secure-SIP (S-SIP). S-SIP consists of two protocols: the SIP authentication (A-SIP) protocol and the key management and protection (KP-SIP) protocol. A-SIP is a novel mutual authentication protocol. KP-SIP is used to secure SIP signaling messages and exchange session keys among entities. It provides different security services for SIP: integrity, confidentiality, and key management. A-SIP is based on the secure remote password (SRP) protocol, which is one of standard password-based authentication protocols supported by the transport layer security (TLS) standard. However, A-SIP is more secure and efficient than SRP because it covers its security flaws and weaknesses, which are illustrated and proven in this work. Through comprehensive informal and formal security analyses, we demonstrate that S-SIP is secure and can address SIP vulnerabilities. In addition, the proposed protocols were compared with many related protocols in terms of security and performance. It was found that the proposed protocols are more secure and have better performance

The Application of Fault-Tolerant Partition Resolvability in Cycle-Related Graphs

The concept of metric-related parameters permeates all of graph theory and plays an important role in diverse networks, such as social networks, computer networks, biological networks and neural networks. The graph parameters include an incredible tool for analyzing the abstract structures of networks. An important metric-related parameter is the partition dimension of a graph holding auspicious applications in telecommunication, robot navigation and geographical routing protocols. A fault-tolerant resolving partition is a preference for the concept of a partition dimension. A system is fault-tolerant if failure of any single unit in the originally used chain is replaced by another chain of units not containing the faulty unit. Due to the optimal fault tolerance, cycle-related graphs have applications in network analysis, periodic scheduling and surface reconstruction. In this paper, it is shown that the partition dimension (PD) and fault-tolerant partition dimension (FTPD) of cycle-related graphs, including kayak paddle and flower graphs, are constant and free from the order of these graphs. More explicitly, the FTPD of kayak paddle and flower graphs is four, whereas the PD of flower graphs is three. Finally, an application of these parameters in a scenario of installing water reservoirs in a locality has also been furnished in order to verify our findings

A Critical Review on Channel Modeling: Implementations, Challenges and Applications

In recent years, the use of massive multiple-input multiple-output (MIMO) systems and higher frequency bands for next-generation urban rail transportation systems has emerged as an intriguing research topic due to its potential to significantly increase network capacity by utilizing available narrowband and broadband spectrums. In metro and mining applications, the high-reliability wireless sensor network (WSN) plays a vital role in providing personal safety, channel optimization, and improving operational performance. Through the duration of 1921–2023, this paper provides the survey on the progress of fifth-generation (5G) and beyond-fifth-generation (B5G) wireless communication systems in underground environments such as tunnels and mines, the evolution of the earliest technologies, development in channel modeling for vehicle-to-vehicle (V2V) communications, and realization of different wireless propagation channels in high-speed train (HST) environments. In addition, the most recent advanced channel modeling methods are examined, including the development of new algorithms and their use in intelligent transportation systems (ITS); mathematical, analytical, and experimental techniques for propagation design; and the significance of the radiation characteristics, antenna placing, and physical environment effect on wireless communications. Leaky coaxial cable (LCX) and distributed antenna system (DAS) designs are introduced in the demonstrated systems for improving the channel capacity of narrowband and wideband channels as well as the spatial characteristics of various MIMO systems. The review article concludes by figuring out open research directions for future technologies

Fault Diagnostics and Tolerance Analysis of a Microgrid System Using Hamilton–Jacobi–Isaacs Equation and Game Theoretic Estimations in Sliding Mode Observers

This paper focuses on robustness and sensitivity analysis for sensor fault diagnosis of a voltage source converter based microgrid model. It uses robust control parameters such as minimum sensitivity parameter (H−), maximum robustness parameter (H∞), and compromised both (H−/H∞), being incorporated in the sliding mode observer theory using the game theoretic saddle point estimation achieved through convex optimization of constrained LMIs. The approach used works in a way that the mentioned robust control parameters are embedded in Hamilton–Jacobi–Isaacs-Equation (HJIE) and are also used to determine the inequality version of HJIE, which is, in terms of the Lyapunov function, faults/disturbances and augmented state/output estimation error as its variables. The stability analysis is also presented by negative definiteness of the same inequality version of HJIE, and additionally, it also gives linear matrix inequalities (LMIs), which are optimized using iterative convex optimization algorithms to give optimal sliding mode observer gains enhanced with robustness to maximal preset values of disturbances and sensitivity to minimal preset values of faults. The enhanced sliding mode observer is used to estimate states, faults, and disturbances using sliding mode observer theory. The optimality of sliding mode observer gains for sensitivity of the observer to minimal faults and robustness to maximal disturbance is a game theoretic saddle point estimation achieved through convex optimization of LMIs. The paper includes results for state estimation errors, faults’ estimation/reconstruction, fault estimation errors, and fault-tolerant-control performance for current and potential transformer faults. The considered faults and disturbances in current and potential transformers are sinusoidal nature composite of magnitude/phase/harmonics at the same time

Enhancement in the Performance of Dye Sensitized Solar Cells (DSSCs) by Incorporation of Reduced Graphene Oxide (RGO) and Carbon Nanotubes (CNTs) in ZnO Nanostructures

In this work, a fast, environment-friendly and economic route was used to prepare ZnO and their nanocomposites containing reduced graphene oxide (RGO) and carbon nanotubes (CNTs) for the fabrication of dye-sensitized solar cells (DSSCs). The prepared nanostructures were well-characterized by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), and Raman measurements. The XRD, Raman and TEM results confirmed that the ZnO nanostructures were crystallized into the hexagonal phase, and the nanocomposites containing RGO and CNTs. Morphological studies performed by using FESEM and TEM images showed that the ZnO possessed tube-like morphology with length and diameter in the range of ~1 micron and 90–200 nm, respectively, which were uniform and densely covered on the surface of the carbon materials. The DSSCs were fabricated using prepared nanostructures as a working electrode and platinum as a counter electrode with ruthenium-based dyes and iodide electrolytes. To further improve the efficiency of fabricated solar cells, nanocomposites of prepared nanostructures of ZnO with RGO and CNTs were synthesized, and their results were compared with the pristine samples. The results showed that the ZnO/CNTs (0.5 wt%) nanocomposites electrode exhibited the highest power conversion efficiency (PCE) of DSSCs with a maximum value of 0.612% compared to 0.326% of DSSC with pure ZnO, and 0.574% of DSSC with ZnO/RGO. Significantly, this technique could be used for large-scale production using the existing economical and highly effective DSSC fabrication technique

Association between Obesity and COVID-19: Insights from Social Media Content

The adoption of emerging technologies in healthcare systems plays a crucial part in anti-obesity initiatives. COVID-19 has intensified the Body Mass Index (BMI) discourses in AI (Artificial Intelligence)-powered social media. However, few studies have reported on the influence of digital content on obesity prevention policies. Understanding the nature and forums of obese metaphors in social media is the first step in policy intervention. The purpose of this paper is to understand the mutual influence between obesity and COVID-19 and determine its policy implications. This paper analyzes the public responses to obesity using Twitter data collected during the COVID-19 pandemic. The emotional nature of tweets is analyzed using the NRC lexicon. The results show that COVID-19 significantly influences perceptions of obesity; this indicates that existing public health policies must be revisited. The study findings delineate prerequisites for obese disease control programs. This paper provides policy recommendations for improving social media interventions in health service delivery in order to prevent obesity