WBSN in IoT health-based application: toward delay and energy consumption minimizing

The wireless body sensor network (WBSN) technologies are one of the essential technologies of the Internet of things (IoT) growths of the healthcare paradigm, where every patient is monitored through a group of small-powered and lightweight sensor nodes. Thus, energy consumption is a major issue in WBSN. The major causes of energy wastage in WBSN are collisions and retransmission process. However, the major cause of the collision happened when two sensors are attempting to transmit data at exactly the same time and same frequency, and the major cause of the retransmission process happened when the collision takes place or data does not received properly due to channel fading. In this paper, we proposed a cognitive cooperative communication with two master nodes, namely, as two cognitive master nodes (TCMN), which can eliminate the collision and reduce the retransmission process. First, a complete study of a scheme is investigated in terms of network architecture. Second, a mathematical model of the link and outage probability of the proposed protocol are derived. Third, the end-to-end delay, throughput, and energy consumption are analyzed and investigated. The simulation and numerical results show that the TCMN can do system performance under general conditions with respect to direct transmission mode (DTM) and existing work

Energy harvesting Internet of Things health-based paradigm: toward outage probability reduction through inter-wireless body area network cooperation

In today’s healthcare environment, the Internet of Things technology provides suitability among physicians and patients, as it is valuable in numerous medicinal fields. Wireless body sensor network technologies are essential technologies in the growth of Internet of Things healthcare paradigm, where every patient is monitored utilising small-powered and lightweight sensor nodes. A dual-hop, inter–wireless body sensor network cooperation and an incremental inter–wireless body sensor network cooperation with energy harvesting in the Internet of Things health-based paradigm have been investigated and designed in this work. The three protocols have been named and abbreviated as follows: energy harvesting–based dual-hop cooperation, energy harvesting–based inter–wireless body sensor network cooperation and energy harvesting–based incremental inter–wireless body sensor network cooperation. Outage probabilities for the three designed protocols were investigated and inspected, and mathematical expressions of the outage probabilities were derived. The simulation and numerical results showed that the energy harvesting–based incremental inter–wireless body sensor network cooperation provided superior performance over the energy harvesting–based inter–wireless body sensor network cooperation and energy harvesting–based dual-hop cooperation by 1.38 times and 5.72 times, respectively; while energy harvesting–based inter–wireless body sensor network cooperation achieved better performance over energy harvesting–based dual-hop cooperation by 1.87 times

Multiple inputs all-optical logic gates based on nanoring insulator-metal-insulator plasmonic waveguides

In this paper, we report new nanoscale plasmonic multiple inputs logic gates based on insulator-metal-insulator (IMI) nanoring waveguides. The proposed all-optical gates are numerically analyzed by the finite element method. NOT, AND, NAND, NOR, and EX-NOR all-optical logic gates were suitably designed and investigated based on the linear interface between the propagated waves through the waveguides. The operation wavelength was 1550 nm. The simulation results show that the optical transmission threshold of (0.26) which performs the operation of planned logic gates is accomplished. Moreover, simulation results show that our compact structure of all-optical logic gates may have potential applications in all-optical integrated networks

Distributed remote antenna unit with selection-based of (RoF-IoT) paradigm: Performance improvement

Data availability statement: All data are available within manuscript.Copyright © 2022 The Authors. Mixing the wireless medium with fiber optics can form a new communication system called a radio-over-fiber (RoF) network; it is a promising solution that can provide high bandwidth and a reliable connection between numerous sensors in wireless sensor networks (WSN) and the central office (base station) within a particular area. This paper first design and discusses new paradigms of fire detection in the IoT environment using RoF technology. Second, this paper covers the distribution of remote antenna unit (DRAU) architecture within RoF-IoT. Finally, best remote antennas unit selection (BRAUS) of distributed RAUs architecture protocols utilizing new selection metrics is proposed. Two important metrics have been analysed and mathematically modelled, outage probability and bandwidth efficiency, respectively. Both metrics have been analysed as a function of the distance, number of RAUs, fiber optic attenuation, and path loss factor. Based on the simulation and numerical analysis, the outage probability of proposed protocols is reduced by 65% compared to recent work; in addition, the bandwidth efficiency of the proposed protocol is increased by 34% compared to recent work.Brunel University London. Grant Number: J066

Cryptography: Advances in Secure Communication and Data Protection

In the innovative work secure communication and data protection are being main field, which are emerged by cryptography as a fundamental pillar. Strong cryptographic methods are now essential given the rising reliance on digital technologies and the threats posed by bad actors. This abstract examines the evolution of secure communication protocols and data protection techniques as it relates to the advancements in cryptography. The development of post-quantum cryptography is the most notable development in cryptography discussed in this study. As quantum computers become more powerful, they pose a serious threat to traditional cryptographic algorithms, such as RSA and ECC. Designing algorithms that are immune to attacks from quantum computers is the goal of post-quantum cryptography. Lattice-based, code-based, and multivariate-based cryptography are only a few of the methods that have been investigated in this context

Anti-Disturbance Compensation-Based Nonlinear Control for a Class of MIMO Uncertain Nonlinear Systems

Multi-Inputs-Multi-Outputs (MIMO) systems are recognized mainly in industrial applications with both input and state couplings, and uncertainties. The essential principle to deal with such difficulties is to eliminate the input couplings, then estimate the remaining issues in real-time, followed by an elimination process from the input channels. These difficulties are resolved in this research paper, where a decentralized control scheme is suggested using an Improved Active Disturbance Rejection Control (IADRC) configuration. A theoretical analysis using a state-space eigenvalue test followed by numerical simulations on a general uncertain nonlinear highly coupled MIMO system validated the effectiveness of the proposed control scheme in controlling such MIMO systems. Time-domain comparisons with the Conventional Active Disturbance Rejection Control (CADRC)-based decentralizing control scheme are also included

Artificial Intelligence in Healthcare: Diagnosis, Treatment, and Prediction

One of the most potential uses of artificial intelligence (AI), which has changed a number of industries, is in healthcare. The application of AI in healthcare is discussed in general in this study, with an emphasis on diagnosis, treatment, and prediction. In the area of diagnostics, AI has proven to be remarkably adept at deciphering X-rays, CT scans, and MRI pictures to spot illnesses and anomalies. A branch of AI known as deep learning algorithms has shown to be particularly good at accurately identifying and categorizing medical disorders. Large volumes of imaging data may be swiftly analyzed by AI systems, enabling medical personnel to diagnose patients more accurately and with fewer mistakes. Additionally, AI may combine patient information, genetic data, and other pertinent data to produce tailored diagnostic suggestions. Consequently, AI has become a game-changing force in healthcare, especially in the disciplines of diagnosis, treatment, and prediction. AI systems can help medical personnel make more precise diagnoses, create individualized treatment plans, and forecast patient outcomes by utilizing machine learning algorithms and advanced data analytics. While there are still difficulties, there are enormous potential advantages for AI in healthcare, and coordinated efforts are required to realize these advantages and assure its ethical and fair incorporation into healthcare systems

TAQWA: Teaching Adolescents Quality Wadhu/Ablution contactlessly using deep learning

This research presents a unique and innovative approach to teaching young children the proper steps of ablution (wazoo/wudu) by utilizing a non-invasive sensing system integrated with deep learning algorithms. However, most existing ablution detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. We conducted experiments with a group of youngsters to evaluate the system’s effectiveness, demonstrating its potential in fostering a deeper appreciation and comprehension of religious practices among young learners. This innovative privacy-preserving ablution system employs state-of-the-art UWB-radar technology with advanced Deep Learning (DL) techniques to effectively address the challenges mentioned above. The core focus of this system is to categorize the four fundamental ablution steps: Wash Face 3x, Wash Hand 3x, Wash Head 1x, and Wash Feet 3x. By transforming the collected data into spectrograms and harnessing the sophisticated DL models VGG16 and VGG19, the proposed system accurately detects these ablution steps, achieving an impressive maximum accuracy of 97.92% across all categories with the utilization of VGG16

Contribution of ABCG2 gene polymorphisms (G34A and C376T) in the prognosis of colorectal cancer

73-78This study aims to assess the association of two single nucleotide polymorphisms (SNPs), G34A and C376T, in the ABCG2 gene with the risk of developing CRC. To the best of our knowledge, this is the first study that determined the role of genetic variations in the ABCG2 gene with the risk of CRC in Saudi Arabia. The gDNA was extracted from the blood of 58 CRC patients and 48 healthy subjects. The DNA sequencing was used to determine the distribution of genotypes. The results showed that CRC patients carried a heterozygous (GA) genotype for SNP G34A had a low risk of developing CRC (odds ratio=0.015, 95% CI [0.00–0.12]; risk ratio=0.35, 95% CI [0.25–0.12], P P 0.0001). In conclusion, the results indicated that a heterozygous (GA) genotype in SNP G34A may decrease the risk of CRC development, whereas, the heterozygous (CT) genotype in SNP C376T may increase the risk of CRC. The results may suggest a protective role of ABCG2 SNP G34A against CRC and a deleterious effect of ABCG2 SNP C376T for increasing the risk of CRC

Contactless privacy-preserving head movement recognition using deep learning for driver fatigue detection

Head movement holds significant importance in con-veying body language, expressing specific gestures, and reflecting emotional and character aspects. The detection of head movement in smart or assistive driving applications can play an important role in preventing major accidents and potentially saving lives. Additionally, it aids in identifying driver fatigue, a significant contributor to deadly road accidents worldwide. However, most existing head movement detection systems rely on cameras, which raise privacy concerns, face challenges with lighting conditions, and require complex training with long video sequences. This novel privacy-preserving system utilizes UWB-radar technology and leverages Deep Learning (DL) techniques to address the mentioned issues. The system focuses on classifying the five most common head gestures: Head 45L (HL45), Head 45R (HR45), Head 90L (HL90), Head 90R (HR90), and Head Down (HD). By processing the recorded data as spectrograms and leveraging the advanced DL model VGG16, the proposed system accurately detects these head gestures, achieving a maximum classification accuracy of 84.00% across all classes. This study presents a proof of concept for an effective and privacy-conscious approach to head position classification.</p