Despite the fact that countless IoT applications are arising frequently in various fields, such as green cities, net-zero decarbonization, healthcare systems, and smart vehicles, the smart grid is considered the most critical cyber–physical IoT application. With emerging technologies supporting the much-anticipated smart energy systems, particularly the smart grid, these smart systems will continue to profoundly transform our way of life and the environment. Energy systems have improved over the past ten years in terms of intelligence, efficiency, decentralization, and ICT usage. On the other hand, cyber threats and attacks against these systems have greatly expanded as a result of the enormous spread of sensors and smart IoT devices inside the energy sector as well as traditional power grids. In order to detect and mitigate these vulnerabilities while increasing the security of energy systems and power grids, a thorough investigation and in-depth research are highly required. This study offers a comprehensive overview of state-of-the-art smart grid cybersecurity research. In this work, we primarily concentrate on examining the numerous threats and cyberattacks that have recently invaded the developing smart energy systems in general and smart grids in particular. This study begins by introducing smart grid architecture, it key components, and its security issues. Then, we present the spectrum of cyberattacks against energy systems while highlighting the most significant research studies that have been documented in the literature. The categorization of smart grid cyberattacks, while taking into account key information security characteristics, can help make it possible to provide organized and effective solutions for the present and potential attacks in smart grid applications. This cyberattack classification is covered thoroughly in this paper. This study also discusses the historical incidents against energy systems, which depicts how harsh and disastrous these attacks can go if not detected and mitigated. Finally, we provide a summary of the latest emerging future research trend and open research issues.

Head-mounted displays (HMDs) have the potential to greatly impact the surgical field by maintaining sterile conditions in healthcare environments. Google Glass (GG) and Microsoft HoloLens (MH) are examples of optical HMDs. In this comparative survey related to wearable augmented reality (AR) technology in the medical field, we examine the current developments in wearable AR technology, as well as the medical aspects, with a specific emphasis on smart glasses and HoloLens. The authors searched recent articles (between 2017 and 2022) in the PubMed, Web of Science, Scopus, and ScienceDirect databases and a total of 37 relevant studies were considered for this analysis. The selected studies were divided into two main groups; 15 of the studies (around 41%) focused on smart glasses (e.g., Google Glass) and 22 (59%) focused on Microsoft HoloLens. Google Glass was used in various surgical specialities and preoperative settings, namely dermatology visits and nursing skill training. Moreover, Microsoft HoloLens was used in telepresence applications and holographic navigation of shoulder and gait impairment rehabilitation, among others. However, some limitations were associated with their use, such as low battery life, limited memory size, and possible ocular pain. Promising results were obtained by different studies regarding the feasibility, usability, and acceptability of using both Google Glass and Microsoft HoloLens in patient-centric settings as well as medical education and training. Further work and development of rigorous research designs are required to evaluate the efficacy and cost-effectiveness of wearable AR devices in the future.

As wind energy is widely available, an increasing number of individuals, especially in off-grid rural areas, are adopting it as a dependable and sustainable energy source. The energy of the wind is harvested through a device known as a wind energy harvesting system (WEHS). These systems convert the kinetic energy of wind into electrical energy using wind turbines (WT) and electrical generators. However, the output power of a wind turbine is affected by various factors, such as wind speed, wind direction, and generator design. In order to optimize the performance of a WEHS, it is important to track the maximum power point (MPP) of the system. Various methods of tracking the MPP of the WEHS have been proposed by several research articles, which include traditional techniques such as direct power control (DPC) and indirect power control (IPC). These traditional methods in the standalone form are characterized by some drawbacks which render the method ineffective. The hybrid techniques comprising two different maximum power point tracking (MPPT) algorithms were further proposed to eliminate the shortages. Furtherly, Artificial Intelligence (AI)-based MPPT algorithms were proposed for the WEHS as either standalone or integrated with the traditional MPPT methods. Therefore, this research focused on the review of the AI-based MPPT and their performances as applied to WEHS. Traditional MPPT methods that are studied in the previous articles were discussed briefly. In addition, AI-based MPPT and different hybrid methods were also discussed in detail. Our study highlights the effectiveness of AI-based MPPT techniques in WEHS using an artificial neural network (ANN), fuzzy logic controller (FLC), and particle swarm optimization (PSO). These techniques were applied either as standalone methods or in various hybrid combinations, resulting in a significant increase in the system’s power extraction performance. Our findings suggest that utilizing AI-based MPPT techniques can improve the efficiency and overall performance of WEHS, providing a promising solution for enhancing renewable energy systems.

Attacks on data stored in mobile devices are increasingly getting more efficient and successful, especially with the use of advanced cryptanalysis techniques and high-tech systems. Encryption using the IEEE XTS-AES algorithm might be an attractive solution for this problem, but it comes with a significant impact on the performance of these limited-resources devices. The emergence of the potential Graphical Processing Units (GPUs), as a general purpose non-graphical computational power, has gained a great interest in both industry and academia. Recently, GPUs have presented higher performance for parallel programming than conventional CPUs while they continue gaining reduced cost. One important application area that can benefit from GPUs power is storage encryption in mobile devices. In this paper, we introduce a GPU-accelerated framework for storage encryption in mobile devices using the XTS-AES encryption algorithm. The Google's Android is targeted in this work as a mobile operating system.

Securing data stored inside the storage devices is becoming an important concern in computer security now. It is known that the most efficient techniques to protect storage devices are using cryptography. Developing newer and more secure encryption algorithms and modes of operation might be critically important to protect these devices since conventional disk encryption algorithms, such as CBC mode, have shown serious security flaws. In this paper, the newly standardized IEEE XTS encryption mode of operation for storage encryption (P1619 standard) has been implemented using parallel design. A performance comparison between the sequential and parallel algorithms of XTS mode has been presented. The parallel XTS algorithm has shown a speedup of 1.80 (with 90% efficiency) faster than the sequential algorithm. In these simulations, AES is used as encryption algorithm with 256-bit encryption key.

Data storage devices such as disk drives and personal storage devices (PSD) such as flash disks are now widely used in everyday appliances. Data breaches have grown rapidly in the recent years due to compromised confidential data from storage devices. These trends forced standardization of encryption methods to secure storage devices. This paper provides a comparison review of existing encryption algorithms and modes of operations that are suitable for securing storage devices, mainly disk drives. The recent IEEE approved standard for encryption mode of operation, XTS will be evaluated in term of performance with different algorithms (AES, Twofish, and RC6). The performance of XTS mode with other modes of operations (LRW and CBC) will be explored. This detailed review and performance analysis will provide guidelines for storage devices designers to implement disk encryption.