The Internet of Things (IoT) has emerged as a technology capable of connecting heterogeneous nodes/objects, such as people, devices, infrastructure, and makes our daily lives simpler, safer, and fruitful. Being part of a large network of heterogeneous devices, these nodes are typically resource-constrained and became the weakest link to the cyber attacker. Classical encryption techniques have been employed to ensure the data security of the IoT network. However, high-level encryption techniques cannot be employed in IoT devices due to the limitation of resources. In addition, node security is still a challenge for network engineers. Thus, we need to explore a complete solution for IoT networks that can ensure nodes and data security. The rule-based approaches and shallow and deep machine learning algorithms- branches of Artificial Intelligence (AI)- can be employed as countermeasures along with the existing network security protocols. This paper presented a comprehensive layer-wise survey on IoT security threats, and the AI-based security models to impede security threats. Finally, open challenges and future research directions are addressed for the safeguard of the IoT network.

Fall causes trauma or critical injury among the geriatric population which is a second leading accidental cause of post-injury mortality around the world. It is crucial to keep elderly people under supervision by ensuring proper privacy and comfort. Thus the elderly fall detection and prediction using wearable/ non-wearable sensors become an active field of research. In this work, a novel pipeline for fall detection based on wearable accelerometer data has been proposed. Three publicly available datasets have been used to validate our proposed method, and more than 7700 cross-disciplinary time-series features were investigated for each of the datasets. After following a series of feature reduction techniques such as mutual information, removing highly correlated features using the Pearson correlation coefficient, Boruta algorithm, we have obtained the dominant features for each dataset. Different classical machine learning (ML) algorithms were utilized to detect falls based on the obtained features. For individual datasets, the simple ML classifiers achieved very good accuracy. We trained our pipeline with two of the three datasets and tested with the remaining one dataset until all three datasets were used as the test set to show the generalization capability of our proposed pipeline. A set of 39 high-performing features is selected, and the classifiers were trained with them. For all the cases, the proposed pipeline showed excellent efficiency in detecting falls. This architecture performed better than most of the existing works in all the used publicly available datasets, proving the supremacy of the proposed data analysis pipeline.

We review the recent advances on the implementation of electronic circuits that operate in the millimeter-wave (30–300 GHz) and terahertz (300–3000 GHz) frequency ranges. The focus of this article is on nonlinear phenomena in electronics. The different implementations of nonlinear circuits for the sake of millimeter-wave and terahertz signal generation are studied in this paper. The challenges of signal generation are examined and the benefits and limitations of different schemes of signal generation are discussed. It is shown that nonlinear devices such as electronic transistors exhibit major advantages enabling realization of low-cost and portable circuits for the emerging applications in these frequency ranges. We also review linear and nonlinear design methodologies employing the properties of electromagnetic waves. The electronic systems designed based on the presented ideas are shown to push the previously unbeatable limits of operation in millimeter-wave and terahertz frequency ranges. A discussion on remaining challenges and future directions concludes the paper.

The coronavirus pandemic (COVID-19) spreads worldwide during the first half of 2020. As is the case for all countries, the Kingdom of Saudi Arabia (KSA), where the number of reported cases reached more than 392 K in the first week of April 2021, was heavily affected by this pandemic. In this study, we introduce a new simulation model to examine the pandemic evolution in two major cities in KSA, namely, Riyadh (the capital city) and Jeddah (the second-largest city). Consequently, this study estimates and predicts the number of cases infected with COVID-19 in the upcoming months. The major advantage of this model is that it is based on real data for KSA, which makes it more realistic. Furthermore, this paper examines the parameters used to understand better and more accurately predict the shape of the infection curve, particularly in KSA. The obtained results show the importance of several parameters in reducing the pandemic spread: the infection rate, the social distance, and the walking distance of individuals. Through this work, we try to raise the awareness of the public and officials about the seriousness of future pandemic waves. In addition, we analyze the current data of the infected cases in KSA using a novel Gaussian curve fitting method. The results show that the expected pandemic curve is flattening, which is recorded in real data of infection. We also propose a new method to predict the new cases. The experimental results on KSA’s updated cases reveal that the proposed method outperforms some current prediction techniques, and therefore, it is more efficient in fighting possible future pandemics.

Terahertz (THz) sources with stabilized output frequencies are desired in many systems, however, the design of conventional THz phase-locked loops encounters many challenges. In this paper, a fully on-chip frequency-stabilization feedback mechanism is proposed, which avoids the use of both frequency dividers and the off-chip reference, achieving much lower power consumption and system integration cost. Using this scheme, two circuit prototypes are designed in a 0.13- μ m SiGe BiCMOS technology. The first one is a 302-332-GHz frequency-stabilized source with ultralow power consumption. It achieves a -13.9-dBm probed output power with a dc power consumption of only 51.7 mW. The second design is a 308-317-GHz frequency-stabilized high-output-power radiating array, which achieves a peak-radiated power and EIRP of 4.6 mW and 24.7 dBm, respectively, with a total dc power consumption of 1.18 W. With significantly lower system integration cost and power consumption, the proposed scheme is attractive for many applications that require short coherence time, such as THz active imaging systems and short-range millimeter-wave (mm-wave)/THz FMCW radars. With further calibration and enhancement in long-term stability, this scheme will also be useful in systems like communication transceivers.