By 2030, wireless communications will have connected over thirty billion devices. Narrowband Internet of Things (NB-IOT) technology has grown in popularity in response to the rapid growth of the internet of things (IOT) sector. The main aim of this study is to supply overall survey of the design modifications transported in the NB-IoT standardization along with comprehensive study evolutions according to popular companies in many countries such as: Telia, Elisa, Orange, Telecom Italia, Telstra, Vodafone, On the other hand, because there is a lot of work in NB-IOT on optimizing parameters or improving optimization methods, Consequently, this work presents a Blockchain architecture that may be used to enhance security, authentication, and efficient data access while maintaining data integrity. We describe the optimization parameters for physical channel and signal transmission and reception. Using the Physical Downlink Control Channel, we devise an adaptation scheme for 200 KHz bandwidth in NB-IOT networks (PDCCH). Finally, we want to locate the following: optimal parameters: Number of frames, Doppler frequency and Diversity performance.

Elections and voting play a crucial role in the development of a democratic society, enabling the public to express their views and participate in the decision-making process. Voting methods have evolved from paper ballot systems to e-voting systems to preserve the integrity of votes, ensuring a secure, transparent, and verifiable process. Continuous efforts have been made to develop a secure e-voting system that eliminates fraud attempts and provides accurate voting results. In this paper, we propose the architecture of a blockchain-based e-voting system called VoteChain. Developed to support the existing voting system in the state of Palestine, VoteChain aims to provide secure e-voting with features such as auditability, verifiability, accuracy, privacy, flexibility, transparency, mobility, availability, convenience, data integrity, and distribution of authority. The work introduces a smart contract designed to meet the demands of e-voting, governing transactions, monitoring computations, enforcing acceptable usage policies, and managing data usage after transmission. The proposed system also adopts advanced cryptographic techniques to enhance security. VoteChain features a web-based interface to facilitate user interaction, providing protection against multiple or double voting to ensure the integrity of the election. Furthermore, VoteChain is designed with a user-friendly and easily accessible administrator interface for managing voters, constituencies, and candidates. It ensures equal participation rights for all voters, fostering fair and healthy competition among candidates while preserving voter anonymity. A comparative analysis demonstrates VoteChain’s advancements in privacy, security, and scalability over both traditional and blockchain-based e-voting systems.

With the rapid advancement of technology and communication, smartphones have become ubiquitous, offering functionalities such as geo-location-based photo capturing through GPS and navigation applications. Digital forensic examiners can retrieve location data from Exchangeable Image File Format (EXIF) metadata embedded in photos, commonly referred to as “geolocation,” which is crucial in criminal investigations. Modern Android smartphones and digital cameras store GPS coordinates in every captured photo, allowing forensic analysts to leverage this information to solve cases. This paper demonstrates the process of manually extracting geographical identification data (latitude, longitude, altitude) from raw image files using Hex editor tools and validating the results with Google Maps. These methods aid forensic investigators and law enforcement agencies by providing evidence that can be presented in court.

The agriculture sector stands as one of the most significant sectors sustaining 70 percent of the world's population. In this sector, the supply consists of a series of interconnected stages, spanning from farming through production to the final delivery of goods to the end customer. A lack of transparency within the supply chain presents the largest gap between suppliers and retailers, such as not ensuring the true value of products or services. This research introduces AgroChain, a Blockchain-based system that is designed to support the Agricultural Supply Chain (ASC) process. For scalability purposes, the proposed AgroChain solution comply with a process model that separates the registry of agricultural records from the record itself. The development of the AgroChain prototype along with its smart contracts that establish a transparent, yet secure, environment within the ASC under Quorum which is Ethereum oriented network is illustrated in this research. This research demonstrates that Blockchain networks offer a solution to manage the ASC ensuring traceability, privacy and integrity. Moreover, the research indicates that there is a pressing need to promote the standardization of ASC smart contracts, incorporating secure and straightforward process. Smart contracts have to ideally be implemented in consortium environments, enabling reliable validation of transactions by independent third parties without necessitating access to their content. Moreover, investigating the accountability for illegal activities become challenging.

Several research on cyberbullying detection have employed different deep learning and machine learning methodologies to achieve promising outcomes. Nevertheless, most of them have primarily concentrated on using English data for both purposes: training and testing, with only a limited number considering native languages such as Arabic. Thus, there is a critical need to address cyberbullying in its native linguistic context. The dataset utilized in this research has been compiled and sourced from various Kaggle and Github repositories. Six collected benchmark datasets from Facebook, Twitter and Instagram in addition to a developed Arabic cyberbullying lexicon were utilized to evaluate the efficiency of the proposed hybrid model. Prior to classification, data cleaning was carried out to preprocess the text. Moreover, word embedding as a natural language processing method is utilized. Numerous machine learning and deep learning algorithms were assessed, encompassing naïve bayes, support vector machines, k-nearest neighbors, decision trees, random forest, multi-layer perceptron neural networks, convolutional neural networks, recurrent neural networks, bidirectional long short-term memory, long short-term memory, and gated recurrent units, with a meticulous comparative analysis conducted. Given their demonstrated potential, hybrid techniques have emerged as promising model for effectively detecting instances of cyberbullying. Thus, the best performing algorithms is utilized to construct the hybrid model. This research introduces a hybrid deep learning model with stacked word embedding. This model consistently outperforms single models in terms of cyberbullying detection. We extensively investigated the performance of the proposed hybrid model across diverse data contexts. Through thorough study and validation, the proposed hybrid model demonstrates enhanced capabilities in feature extraction and accurate text classification.

Raed Daraghma, Eman Daraghmi, Yousef Daraghmi, Hacene FoThis paper is the first to integrate a blockchain system to improve the manufacturing efficiency and reliability of 5G F Inverted antennas. The recommended antenna is constructed from a single side of a premium aluminum conductor, the width of the radiator is 0.564 mm, the thickness of the conductor is 0.77 mm ground plane is 29.3 x 29.3 mm2 dimension. The antenna is designed to work at a frequency of 5.9 GHz, therefore it can be used for vehicle applications. It is designed to be inserted as an integrated antenna into an IOT device and is composed of Microstrip F shapes. Therefore, a rectangular Microstrip patch F antenna was built and its performance was examined in this work. 5.9 GHz is the antenna's resonance frequency range, which is suitable for vehicle applications. The simulation software for this work was Computer Simulation Technology (CST) software. Antenna performance was compared concerning gain, bandwidth, and return on loss. By enhancing the production efficiency and reliability of 5G Inverted F antennas, our study sets a new benchmark for the integration of blockchain and smart contract technologies, paving the way for ground-breaking advancements in manufacturing techniques. The relevance of creating an inverted F antenna for the Vehicle Systems environment is increased by this research. Typically, rod antennas are found in automobiles. However, the Vehicle Systems industry's requirements for meeting the demands of human resources with a variety of applications at different frequencies are not met by the rod antenna now in use. Because of their important characteristics, which include wideband matching, omnidirectional pattern, high efficiency, and compact size, microstrip patch Inverted F antennas are highly favored in the Vehicle Systems industry. uchal

Steganography is a method used to conceal information, while steganalysis focuses on detecting hidden data. In today's digital landscape, steganography is often used across open communication channels, embedding files, videos, messages, and images within other files to obscure their content from unintended viewers. However, cybercriminals exploit these techniques to covertly transmit data to various devices. Traditional endpoint antimalware tools are not typically designed to search for hidden data, making the detection of steganographic content challenging. The ease with which cybercriminals can transmit data using this method highlights its potential threat. This paper reviews various steganalysis tools and explores the integration of antivirus programs for real-time detection to enhance data confidentiality. A proof-of-concept for one of the steganalysis tools is also provided.

The aim of the paper was to explore the effectiveness of NB-IOT Narrowband Physical Downlink Shared Channel (NPDSCH), which the 3GPP released in version 13 to serve as a mobile communications feature. When compared to previous cellular technologies, NB-expanded IOT's coverage, data rate, latency, and battery lifetime are its key characteristics. These NB-IOT capabilities make it extremely useful for IOT manufacturing and enable the upcoming technology that may be applied in a variety of situations of deployments, including Agriculture, smart cities, and health, and WSNs. The primary goal of this survey is to evaluate the execution of various NB-IOT grid characteristics with acceptable rates of error in uplink and downlink connections. The effectiveness of the distinct approaches was estimated based on how well they met the requirements of IOT sector. In order to evaluate the various criterion sets and determine which options offer the optimal cost-efficiency trade-offs for building an NB-IOT grid, software simulations were employed. According to the findings, data that is sent in a reduced Transport Block Size (TBS) units experiences less errors than data that is sent in a bigger size units. The outcomes also reveal that, in the propagation channel model, the ER rises as the Doppler frequency rises. The findings also demonstrate that as the subject of modulation and coding scheme (IMCS) increases, the ER rises; finally, the results indicate that an increasing number of antennas is nearly optimal for all parameters.

The development histories of NB-IOT, Lora WAN, and Sigfox account for many of their distinctions. While NB-IOT is an open, worldwide, LTE-based 5G industry standard, Lora WAN and Sigfox are proprietary technologies created by different firms. As a result, it has the support of all the major telecommunications companies, hardware manufacturers, and network vendors. In contrast to Lora WAN and Sigfox, NB-IOT is always managed by wireless network operators and it operates on permitted LTE frequency bands. The user should select one of three deployment options for Lora WAN. Through MATLAB simulations, it is demonstrated that the Lora WAN protocol has increased energy efficiency and it is useful for extending the life of networks, approximately three times more than NB-IOT and ten times more than Sigfox protocol. The Lora WAN is shown to have a stability period that is 8.1 better than Sigfox and 3.1 better than NB-IOT through results. The Lora WAN has a higher throughput (which is determined by the amount of packets received at the base station) than NB-IOT and Sigfox.

Mobile forensics is crucial in reconstructing various everyday activities accomplished through mobile applications during an investigation. Manual analysis can be tedious, time-consuming, and error-prone. This study introduces an automated tool called Forensic Operations for Recognizing SQLite Content (FORC), specifically designed for Android, to extract Simple Query Language Table Database Lightweight (SQLite) evidence. SQLite is a library that serves as a container for mobile application data, employing a zero-configuration, serverless, self-contained, and transactional SQL database engine. While some SQLite files possess extensions such as .db, .db3, .sqlite, and .sqlit3, others have none. The lack of file extensions may result in missing evidence that could unveil the truth. The proposed tool utilizes both the file extensions and headers of the SQLite data to recognize and identify SQLite data generated or modified by a mobile application. The FORC tool’s capability was evaluated using the Chrome application as a case study, and a comparison between FORC and other tools was conducted. The results suggest that FORC significantly simplifies mobile forensic analysis.

Software development methods have been evolved to enable producing usable systems rapidly while considering all requirements. Several studies have focused on the need to balance between rapid development and capturing requirements related to user experience and business workflow. This balance has become more urging during COVID19 because many businesses want to quickly transfer to usable electronic systems that are accurate, efficient, easy to learn, satisfy users and support remote work. Therefore, this paper proposes a framework by integrating Rapid Application Development (RAD) method with Participatory Design (PD) method for enabling rapid production of usable systems. Both RAD and PD consist of design stages that can overlap and generate new phases where users participate in the design process and accelerate the production. Five usability tests are also added to the framework to validate the usability of the design at all stages. The Action Research method is used to assess the framework empirically in a context of an urgent need to an electronic system, and qualitative data analyses were conducted. The results show that the framework can be adopted by software companies because it satisfies the requirements of adopting software development methods. Also, the system developed using the framework is usable. The paper concludes that COVID19 affects software development by emphasizing rapid development while maintaining workflow. Also, using video conference for remote design assists in meeting users more frequently and in creating concise requirement documentation.

The saga pattern manages transactions and maintains data consistency across distributed microservices via utilizing local sequential transactions that update each service and publish messages to trigger the next ones. Failure by one transaction causes the execution of compensating transactions that counteract the preceding one. However, saga lacks isolation, meaning that reading and writing data from an incomplete transaction is allowed. Therefore, this research proposes an enhanced saga pattern that resolves the lack of isolation issue via the use of the quota cache and the commit-sync service. Some transactions will be transferred from the database layer to the memory layer. Thus, no wrong commit to the main database will occur. If a microservice fails to be completed, the other microservices will run compensation transactions to rollback the changes that only affect the cache layer instead of the database layer. Database commit will be performed when all transactions are completed successfully. A lightweight microservices-based e-commerce system was implemented for comparison. Experiments were conducted for validation and evaluation. Results demonstrate that the proposal has the capability of resolving the lack of isolation. Results indicate that the proposal achieves better performance not only in typical cases but also in the scenario that needs to handle exceptions.

Covid-19 has led to universities and schools lock down across the world. Education has transformed dramatically from physical classroom learning to e-learning, whereby teaching is done remotely on digital platforms. During the pandemic, several solutions had been suggested in order to foster e-learning. Augmented Reality (AR) is a promising solution which plays a distinctive role in e-learning and has turned to be one of the most interesting learning approach. This research investigated the students’ attitude and acceptance toward utilizing augmented reality applications in e-learning under the “Technology Acceptance Model” (TAM). Results indicate that perceived ease of use, user satisfaction, perceived usefulness, attitude, social influence, environmental variables, perceived enjoyment along with perceived the quality of AR-based applications including the quality of the system, services and information are determinant factors. Additionally, findings indicate that there exists a significant influence of gender differences on the acceptance of AR-based applications in e-learning among female and male students.

This paper proposes a three-computing-layer architecture consisting of Edge, Fog, and Cloud for remote health vital signs monitoring. The novelty of this architecture is in using the Narrow-Band IoT (NB-IoT) for communicating with a large number of devices and covering large areas with minimum power consumption. Additionally, the architecture reduces the communication delay as the edge layer serves the health terminal devices with initial decisions and prioritizes data transmission for minimizing congestion on base stations. The paper also investigates different authentication protocols for improving security while maintaining low computation and transmission time. For data analysis, different machine learning algorithms, such as decision tree, support vector machines, and logistic regression, are used on the three layers. The proposed architecture is evaluated using CloudSim, iFogSim, and ns3-NB-IoT on real data consisting of medical vital signs. The results show that the proposed architecture reduces the NB-IoT delay by 59.9%, the execution time by an average of 38.5%, and authentication time by 35.1% for a large number of devices. This paper concludes that the NB-IoT combined with edge, fog, and cloud computing can support efficient remote health monitoring for large devices and large areas.

The violation traffic laws by driving at high speeds, the overloading of passengers, and the unfastening of seatbelts are of high risk and can be fatal in the event of any accident. Several systems have been proposed to improve passenger safety, and the systems either use the sensor-based approach or the computer-vision-based approach. However, the accuracy of these systems still needs enhancement because the entire road network is not covered; the approaches utilize complex estimation techniques, and they are significantly influenced by the surrounding environment, such as the weather and physical obstacles. Therefore, this paper proposes a novel IoT-based traffic violation monitoring system that accurately estimates the vehicle speed, counts the number of passengers, and detects the seatbelt status on the entire road network. The system also utilizes edge computing, fog computing, and cloud computing technologies to achieve high accuracy. The system is evaluated using real-life experiments and compared with another system where the edge and cloud layers are used without the fog layer. The results show that adding a fog layer improves the monitoring accuracy as the accuracy of passenger counting rises from 94% to 97%, the accuracy of seatbelt detection rises from 95% to 99%, and the root mean square error of speed estimation is reduced from 2.64 to 1.87.

The COVID-19 pandemic has negatively affected aspects of human life and various sectors, especially the health sector. These conditions led to the creation of new patterns of life that people have had to deal with to reduce the spread of the epidemic by committing to social distancing, among others. Therefore, governments and technological organizations had to take advantage of technological developments in the current era to overcome these challenges that were created by these conditions. In this paper, we will discuss the role of the blockchain in combating the COVID-19 crisis. Then we will review the recently recorded blockchain-based research proposals to control the COVID-19 pandemic. Finally, we will highlight the challenges of using blockchain to combat the COVID-19 pandemic and find solutions to mitigate these challenges.

This study proposes a Client-Fog-Cloud (CFC) multilayer data processing and aggregation framework that is designed to promote latency-sensitive applications in an IoT context. The framework is designed to address the current IoT-based challenges: wide distribution, massive uploading, low latency, and real-time interaction. The proposed framework consists of the device gateway, the fog server and the cloud. The device gateway collects data from clients and uploads it to the nearest fog node. Received data will be pre-processed and filtered by the fog server before being transferred to the cloud for further processing or storage. An abduction alert fog-based service was implemented to evaluate the proposed framework. Performance was evaluated by comparing the response time and the delay time of the proposed architecture with the traditional cloud computing architecture. Additionally, the aggregation rate was evaluated by simulating the speed of bike riding as well as the walking speed of young adults and elderly. Results show that comparing with the traditional cloud, our proposal noticeably reduces the average response time and the delay time (i.e., whether the newest data or the historical data are being queried). Results indicate the capability of the proposed framework to reduce the response time by 32% and the data transferred to the cloud by 30%.

Research publications are reaching a stunning growth rate. Therefore, new challenges regarding managing the peer-review activities are presented, such as data security, privacy, integrity, fragmentation, and isolation. Further, because of the emergence of predatory journals and research fraud, there is a need to assess the quality of the peer-review process. This research proposes a fully functional blockchain-based editorial management system, namely, TimedChain, for managing the peer-review process from submission to publication. TimedChain provides secure, interoperable, transparent, and efficient access to manuscripts by publishers, authors, readers, and other third parties. Time-based smart contracts and advanced encryption techniques are employed for governing transactions, controlling access, and providing further security. An incentive mechanism that evaluates publishers’ value respecting their efforts at managing and maintaining research data and creating new blocks is introduced. Extensive experiments were conducted for performance evaluation. Results demonstrate the efficiency of the proposed system in governing a large set of data at low latency.

Smart energy requires accurate and effificient short-term electric load forecasting to enable effificient energy management and active real-time power control. Forecasting accuracy is inflfluenced by the char acteristics of electrical load particularly overdispersion, nonlinearity, autocorrelation and seasonal patterns. Although several fundamental forecasting methods have been proposed, accurate and effificient forecasting methods that can consider all electric load characteristics are still needed. Therefore, we propose a novel model for short-term electric load forecasting. The model adopts the negative binomial additive models (NBAM) for handling overdispersion and capturing the nonlinearity of electric load. To address the season ality, the daily load pattern is classifified into high, moderate, and low seasons, and the autocorrelation of load is modeled separately in each season. We also consider the effificiency of forecasting since the NBAM captures the behavior of predictors by smooth functions that are estimated via a scoring algorithm which has low computational demand. The proposed NBAM is applied to real-world data set from Jericho city, and its accuracy and effificiency outperform those of the other models used in this context.

Although the blockchain technology was first introduced through Bitcoin, extending its usage to non-financial applications, such as managing electronic medical records, is an attractive mission for recent research to balance the needs for increasing data privacy and the regular interaction among patients and health providers. Various systems that adopts the blockchain in managing medical records have been proposed. However, there is a need for more work to better characterize, understand and evaluate the employment of blockchain technology in the healthcare industry. In this paper, a design of blockchain based system, namely MedChain, for managing medical records is proposed. MedChain is designed to improve the current systems as it provides interoperable, secure, and effective access for medical records by patients, health care providers, and other third parties, while keeping the patients' privacy. MedChain employs timed-based smart contracts for governing transactions and controlling accesses to electronic medical records. It adopts advanced encryption techniques for providing further security. This work proposes a new incentive mechanism that leverages the degree of health providers regarding their efforts on maintaining medical records and creating new blocks. Extensive experiments are conducted to evaluate the MedChain performance, and results indicate the efficiency of our proposal in handling a large dataset at low latency.