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This study conducts a comparative analysis of the performance of ten novel and well-performing metaheuristic algorithms for parameter estimation of solar photovoltaic models. This optimization problem involves accurately identifying parameters that reflect the complex and nonlinear behaviours of photovoltaic cells affected by changing environmental conditions and material inconsistencies. This estimation is challenging due to computational complexity and the risk of optimization errors, which can hinder reliable performance predictions. The algorithms evaluated include the Crayfish Optimization Algorithm, the Golf Optimization Algorithm, the Coati Optimization Algorithm, the Crested Porcupine Optimizer, the Growth Optimizer, the Artificial Protozoa Optimizer, the Secretary Bird Optimization Algorithm, the Mother Optimization Algorithm, the Election Optimizer Algorithm, and the Technical and Vocational Education and Training-Based Optimizer. These algorithms are applied to solve four well-established photovoltaic models: the single-diode model, the double-diode model, the triple-diode model, and different photovoltaic module models. The study focuses on key performance metrics such as execution time, number of function evaluations, and solution optimality. The results reveal significant differences in the efficiency and accuracy of the algorithms, with some algorithms demonstrating superior performance in specific models. The Friedman test was utilized to rank the performance of the various algorithms, revealing the Growth Optimizer as the top performer across all the considered models. This optimizer achieved a root mean square error of 9.8602187789E−04 for the single-diode model, 9.8248487610E−04 for both the double-diode and triple-diode models and 1.2307306856E−02 for the photovoltaic module model. This consistent success indicates that the Growth Optimizer is a strong contender for future enhancements aimed at further boosting its efficiency and effectiveness. Its current performance suggests significant potential for improvement, making it a promising focus for ongoing development efforts. The findings contribute to the understanding of the applicability and performance of metaheuristic algorithms in renewable energy systems, providing valuable insights for optimizing photovoltaic models. [ABSTRACT FROM AUTHOR]

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Hybridizing metaheuristic algorithms involves synergistically combining different optimization techniques to effectively address complex and challenging optimization problems. This approach aims to leverage the strengths of multiple algorithms, enhancing solution quality, convergence speed, and robustness, thereby offering a more versatile and efficient means of solving intricate real-world optimization tasks. In this paper, we introduce a hybrid algorithm that amalgamates three distinct metaheuristics: the Beluga Whale Optimization (BWO), the Honey Badger Algorithm (HBA), and the Jellyfish Search (JS) optimizer. The proposed hybrid algorithm will be referred to as BHJO. Through this fusion, the BHJO algorithm aims to leverage the strengths of each optimizer. Before this hybridization, we thoroughly examined the exploration and exploitation capabilities of the BWO, HBA, and JS metaheuristics, as well as their ability to strike a balance between exploration and exploitation. This meticulous analysis allowed us to identify the pros and cons of each algorithm, enabling us to combine them in a novel hybrid approach that capitalizes on their respective strengths for enhanced optimization performance. In addition, the BHJO algorithm incorporates Opposition-Based Learning (OBL) to harness the advantages offered by this technique, leveraging its diverse exploration, accelerated convergence, and improved solution quality to enhance the overall performance and effectiveness of the hybrid algorithm. Moreover, the performance of the BHJO algorithm was evaluated across a range of both unconstrained and constrained optimization problems, providing a comprehensive assessment of its efficacy and applicability in diverse problem domains. Similarly, the BHJO algorithm was subjected to a comparative analysis with several renowned algorithms, where mean and standard deviation values were utilized as evaluation metrics. This rigorous comparison aimed to assess the performance of the BHJO algorithm about its counterparts, shedding light on its effectiveness and reliability in solving optimization problems. Finally, the obtained numerical statistics underwent rigorous analysis using the Friedman post hoc Dunn's test. The resulting numerical values revealed the BHJO algorithm's competitiveness in tackling intricate optimization problems, affirming its capability to deliver favorable outcomes in challenging scenarios. [ABSTRACT FROM AUTHOR]

The growing depletion of fossil fuels has led to the use of distributed renewable energy sources. This shift has altered the grid structure from centralized to distributed, where energy flows from multiple sources through multiple paths, besides producing a more competitive and dynamic energy market, posing new problems to power system management. To tackle the issue of effectively utilizing renewable energy sources, the energy internet (EI) was developed, in which devices are connected by energy routers. Therefore, efficiently transmitting energy within the EI has become a prominent topic in research. Despite the existence of algorithms and reviews in the literature on energy routing, there is still a lack of a thorough and comprehensive comparison of these existing algorithms. This paper classifies current energy routers and discusses and categorizes energy routing algorithms based on their methods. Additionally, the paper conducts extensive simulations to compare several energy routing algorithms in detail. [ABSTRACT FROM AUTHOR]

Clustering is one of the most important approaches used to extend the lifetime of Wireless Sensor Networks (WSN). The fundamental metric taken by clustering algorithms is energy enhancement. Moreover, network coverage and load balance are two important approaches that play crucial roles in improving network lifetime and delivery since the former focuses on maximizing the use of all network resources, while the second is based on distributing the load between the nodes to enhance the energy consumption. As the challenge of clustering nodes in an energy-efficient way is an NP-Hard problem, firefly optimization algorithm is used to address this challenge. However, the proposed solutions focus on centralized processing of the algorithm, which makes them unsuitable for large-scale WSN. In this paper, a double firefly based efficient clustering solution is proposed for large-scale WSN which is implemented in a decentralized fashion to improve the lifetime and packet delivery. The first firefly algorithm is used by each node to move to the best initial Cluster Head (CH) by performing a balance of belonging between the clusters, while the second algorithm is used only between the initial CHs to eliminate membership redundancy and optimally construct balanced clusters. The simulation results show that our proposed solution significantly improves the network lifetime as well as the delivery rate. [ABSTRACT FROM AUTHOR]

Machine learning (ML) represents one of the main pillars of the current digital era, specifically in modern real-world applications. The Internet of Things (IoT) technology is foundational in developing advanced intelligent systems. The convergence of ML and IoT drives significant advancements across various domains, such as making IoT-based security systems smarter and more efficient. However, ML-based IoT systems are vulnerable to lurking attacks during the training and testing phases. An adversarial attack aims to corrupt the ML model's functionality by introducing perturbed inputs. Consequently, it can pose significant risks leading to devices' malfunction, services' interruption, and personal data misuse. This article examines the severity of adversarial attacks and accentuates the importance of designing secure and robust ML models in the IoT context. A comprehensive classification of adversarial machine learning (AML) is provided. Moreover, a systematic literature review of the latest research trends (from 2020 to 2024) of the intersection of AML and IoT-based security systems is presented. The results revealed the availability of various AML attack techniques, where the Fast Gradient Signed Method (FGSM) is the most employed. Several studies recommend the adversarial training technique to defend against such attacks. Finally, potential open issues and main research directions are highlighted for future consideration and enhancement. [ABSTRACT FROM AUTHOR]

The capability to accurately detect web application attacks, especially in a timely fashion, is crucial but remains an ongoing challenge. This study provides an in-depth evaluation of 19 traditional machine learning techniques for detecting web application attacks. The evaluation was conducted across three distinct experiments on refined datasets derived from the HTTPCSIC 2010 dataset. The experiments investigated the performance of these algorithms in different scenarios (e.g., without Knowledge Graph integration, and with KG integration with node2vec feature enhancement). The experimental results revealed that neural network classifiers, notably the Multilayer Perceptron, consistently outperformed other models, achieving accuracy of above 0.90 and maintaining a balanced performance across various metrics. Furthermore, the findings demonstrated that certain algorithms, such as tree-based ensemble methods showed an increase of over 10% in accuracy and Gaussian Process models which exhibited a remarkable improvement in accuracy, rising from 0.84 to 0.99, and in AUC from 0.91 to 1.00, when integrated with the Knowledge Graph, effectively utilizing the additional contextual information. We also found that the KNN classifier demonstrated more than a 16% increase in accuracy. All classifiers showed significant improvements in AUC and other metrics mentioned in our study, indicating that KG integration not only enhances the detection capabilities but also enriches the overall analytical performance of the models. We also observed that linear classifiers and Naive Bayes models generally experienced a decline in performance, highlighting the importance of carefully evaluating the inherent characteristics and capabilities of each algorithm for the web attack detection task. [ABSTRACT FROM AUTHOR]

This study introduces an FER-based machine learning framework for real-time QoE assessment in video streaming. This study's aim is to address the challenges posed by end-to-end encryption and video advertisement while enhancing user QoE. Our proposed framework significantly outperforms the base reference, ITU-T P.1203, by up to 37.1% in terms of accuracy and 21.74% after attribute selection. Our study contributes to the field in two ways. First, we offer a promising solution to enhance user satisfaction in video streaming services via real-time user emotion and user feedback integration, providing a more holistic understanding of user experience. Second, high-quality data collection and insights are offered by collecting real data from diverse regions to minimize any potential biases and provide advertisement placement suggestions. [ABSTRACT FROM AUTHOR]

Wireless body area networks (WBANs) have emerged as a promising solution for addressing challenges faced by elderly individuals, limited medical facilities, and various chronic medical conditions. WBANs consist of wearable sensing and computing devices interconnected through wireless communication channels, enabling the collection and transmission of vital physiological data. However, the energy constraints of the battery-powered sensor nodes in WBANs pose a significant challenge to ensuring long-term operational efficiency. Two-hop routing protocols have been suggested to extend the stability period and maximize the network's lifetime. These protocols select appropriate parent nodes or forwarders with a maximum of two hops to relay data from sensor nodes to the sink. While numerous energy-efficient routing solutions have been proposed for WBANs, reliability has often been overlooked. Our paper introduces an energy-efficient routing protocol called a Hybrid Clustering Approach for Extending WBAN Lifetime (HCEL) to address these limitations. HCEL leverages a utility function to select parent nodes based on residual energy (RE), proximity to the sink node, and the received signal strength indicator (RSSI). The parent node selection process also incorporates an energy threshold value and a constrained number of serving nodes. The main goal is to extend the overall lifetime of all nodes within the network. Through extensive simulations, the study shows that HCEL outperforms both Stable Increased Throughput Multihop Protocol for Link Efficiency (SIMPLE) and Energy-Efficient Reliable Routing Scheme (ERRS) protocols in several key performance metrics. The specific findings of our article highlight the superior performance of HCEL in terms of increased network stability, extended network lifetime, reduced energy consumption, improved data throughput, minimized delays, and improved link reliability. [ABSTRACT FROM AUTHOR]

Emojis have become a crucial part of text-based communication in recent years, especially on social media and messaging services. As a result, emoji prediction has gained increasing attention as a research topic in Natural Language Processing. Emoji recommendation is a task of predicting relevant emojis based on the emotional and contextual orientation of the text. In this study, we provide a comparative analysis of several Machine Learning (ML) and Deep Learning (DL) methods for emoji prediction from Arabic text. ML models are commonly used as baselines for emoji prediction; hence, more sophisticated DL models are needed for performance enhancement. In this work, we evaluate the performance of three baseline ML models, namely Support Vector Machines (SVM), Multinomial Naive Bayes (MNB), and Random Forest (RF), as well as state-of-art DL models, namely Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BiLSTM), Arabic Bidirectional Encoder Representations from Transformers (AraBERT), and Multilingual Bidirectional Encoder Representations from Transformers (mBERT). This research is evaluated utilizing a large corpus of Twitter dataset that is translated to Arabic and balanced to enhance the prediction performance. Throughout the experiments, the ML models achieved classification accuracies of 74%, 78.9%, and 84% for SVM, MNB, and RF, respectively. Furthermore, the DL models achieved accuracies of 91.16%, 91%, 85%, and 80% for LSTM, BiLSTM, AraBERT, and mBERT, respectively. [ABSTRACT FROM AUTHOR]

Misdiagnosis is a critical issue in healthcare, which can lead to severe consequences for patients, including delayed or inappropriate treatment, unnecessary procedures, psychological distress, financial burden, and legal implications. To mitigate this issue, we propose using deep learning algorithms to improve diagnostic accuracy. However, building accurate deep learning models for medical diagnosis requires substantial amounts of high-quality data, which can be challenging for individual healthcare sectors or organizations to acquire. Therefore, combining data from multiple sources to create a diverse dataset for efficient training is needed. However, sharing medical data between different healthcare sectors can be problematic from a security standpoint due to sensitive information and privacy laws. To address these challenges, we propose using blockchain technology to provide a secure, decentralized, and privacy-respecting way to share locally trained deep learning models instead of the data itself. Our proposed method of model ensembling, which combines the weights of several local deep learning models to build a single global model, that enables accurate diagnosis of complex medical conditions across multiple locations while preserving patient privacy and data security. Our research demonstrates the effectiveness of this approach in accurately diagnosing three diseases (breast cancer, lung cancer, and diabetes) with high accuracy rates, surpassing the accuracy of local models and building a multi-diagnosis application. [ABSTRACT FROM AUTHOR]

Smart city development is profoundly impacted by cuttingedge technologies such as information and communications technology (ICT), artificial intelligence (AI), and the Internet of Things (IoT). The intelligent transportation system (ITS) is one of the main requirements of a smart city. The application of machine learning (ML) technology in the development of driver assistance systems, has improved the safety and the comfort of the experience of traveling by road. In this work, we propose an intelligent driving system for road accident risks prediction that can extract maximum required information to alert the driver in order to avoid risky situations that may cause traffic accidents. The current acceptable Internet-of-vehicle (IOV) solutions rely heavily on the cloud, as it has virtually unlimited storage and processing power. However, the Internet disconnection problem and response time are constraining its use. In this case, the concept of vehicular edge computing (V.Edge.C) can overcome these limitations by leveraging the processing and storage capabilities of simple resources located closer to the end user, such as vehicles or roadside infrastructure. We propose an Intelligent and Collaborative Cloud-V.Edge Driver Assistance System (ICEDAS) framework based on machine learning to predict the risks of traffic accidents. The proposed framework consists of two models, CLOUD_DRL and V.Edge_DL, Each one complements the other. Together, these models work to enhance the effectiveness and accuracy of crash prediction and prevention. The obtained results show that our system is efficient and it can help to reduce road accidents and save thousands of citizens' lives. [ABSTRACT FROM AUTHOR]

Users' security is one of the most important issues in Internet of Things (IoT) due to the high number of IoT devices involved in different applications. Security threats are evolving at a rapid pace that make the current security and privacy measures unsuitable. Therefore, several researchers have been attracted by this domain with the aim of proposing either new or improved solutions to address the problem of security in IoT. Blockchain technology is a relatively new invention in modern IoT applications to solve the security issue. It is based on the use of a public immutable ledger called a blockchain. After conducting a verification process, several parts on a network encode transactions into this ledger. Moreover, Machine learning (ML) algorithms have been used as emerging solutions to improve IoT security. Reinforcement learning (RL) is the most popular machine learning technique proposed to secure IoT systems. Unlike other ML methods, RL can observe, learn and interact with the environment even if it has minimum information about the considered parameters. Various researches have been proposed to treat security problem in IoT based on either RL technique or Blockchain technology or a combination of both techniques. Therefore, we believe there is a need for a comprehensive survey on works proposed in recent years that address security issues using these techniques. In this paper, we provide a summary of research efforts made in the past few years, from 2018 to 2021, addressing security issues using RL and blockchain techniques in the IoT domain. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive systematic review of personalized learning software systems. All the systems under review are designed to aid educational stakeholders by personalizing one or more facets of the learning process. This is achieved by exploring and analyzing the common architectural attributes among personalized learning software systems. A literature-driven taxonomy is recognized and built to categorize and analyze the reviewed literature. Relevant papers are filtered to produce a final set of full systems to be reviewed and analyzed. In this meta-review, a set of 72 selected personalized learning software systems have been reviewed and categorized based on the proposed personalized learning taxonomy. The proposed taxonomy outlines the three main architectural components of any personalized learning software system: learning environment, learner model, and content. It further defines the different realizations and attributions of each component. Surveyed systems have been analyzed under the proposed taxonomy according to their architectural components, usage, strengths, and weaknesses. Then, the role of these systems in the development of the field of personalized learning systems is discussed. This review sheds light on the field's current challenges that need to be resolved in the upcoming years. [ABSTRACT FROM AUTHOR]

AADL—architecture analysis and design language—has proven to be important in developing real‐time embedded systems. However, its formalization capability is still limited, which makes it difficult to produce a consistent model; particularly, in the case where several models are combined together. Therefore, using formal verification is an effective way to meet the consistency requirements of the AADL model. This paper proposes a model‐based engineering approach that uses timed automata (TA) formalism to aggregate multiple AADL models into a single composite model. Based on the set of input AADL models, the proposed approach creates a consistent composed model as well as support model checking verification. The effectiveness of this contribution is evaluated through two case studies; a multicamera surveillance system and a drone system. [ABSTRACT FROM AUTHOR]

Internet of Drones (IoD) plays a crucial role in the future Internet of Things due to its important features such as low cost, high flexibility, and mobility. The number of IoD applications is drastically increasing from military to civilian fields. Nevertheless, drones are resource-constrained and highly vulnerable to several security threats and attacks. The use of blockchain technology for securing IoD networks has gained growing attention. To this end, this paper presents a systematic literature review to analyze the current research area regarding the security of IoD environments using the emerging blockchain technology. Forty relevant studies were selected from 129 published articles to answer the identified research questions. The selected studies were classified into three main classes based on blockchain type. Furthermore, a comparison of the reviewed articles in terms of different factors is provided. The research findings show that the blockchain can guarantee fundamental security requirements such as authentication, privacy-preserving, confidentiality, integrity, and access control. Finally, open issues and challenges related to the combination of blockchain and IoD technologies are discussed. [ABSTRACT FROM AUTHOR]

According to the World Health Organization updates, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a pandemic between 2019 and 2022, with millions of confirmed cases and deaths worldwide. There are various approaches to predicting the suspected, infected, and recovered (SIR) cases with different factual or epidemiological models. Some of the recent approaches to predicting the COVID-19 outbreak have had positive impacts in specific nations. Results show that the SIR model is a significant tool to cast the dynamics and predictions of the COVID-19 outbreak compared to other epidemic models. In this paper, we employ the Monte Carlo simulation to predict the spread of COVID-19 in the United Arab Emirates. We study traditional SIR models in general and focus on a time-dependent SIR model, which has been proven more adaptive and robust in predicting the COVID-19 outbreak. We evaluate the time-dependent SIR model. Then, we implement a Monte Carlo model. The Monte Carlo model uses the parameters extracted from the Time-Dependent SIR Model. The Monte Carlo model exhibited a better prediction accuracy and resembles the data collected from the Ministry of Cabinet Affairs, United Arab Emirates, between April and July 2020. [ABSTRACT FROM AUTHOR]

Internet of things (IoT) allows the interconnection between physical devices to improve human lives by offering daily services intelligently. The IoT devices are embedded with sensors and actuators to control our physical world and share personal information via wireless channels. To secure data transmission in the IoT, privacy-preserving is highly necessary. Conventional cryptographic techniques are basic tools to secure wireless communications. However, they cannot be adopted in IoT applications because of resource-constrained devices. Bio-inspired cryptography optimizes conventional cryptography and provides lightweight security solutions for resource-constrained devices. Recently, an encryption security scheme was proposed to secure multimedia communications in IoT. In this paper, we demonstrate that this scheme is prone to several security attacks. Then, we propose a bio-inspired security scheme to achieve privacy-preserving in the IoT. The improved scheme is based on a genetic algorithm and a chaotic system to encrypt/decrypt multimedia data. We formally verified the security of the proposed scheme using the AVISPA tool and evaluated its performance using NS-3. The security and performance results show that our proposed scheme is more secure and efficient than recent related methods. [ABSTRACT FROM AUTHOR]

Recent advances in networking and the emergence of the Internet of Things (IoT) have facilitated the development of the Internet of Vehicles (IoV), a distributed network characterized by large number of connected nodes, such as vehicles, roadside units, and smart devices. The large number of nodes may cause problems, such as network congestion, that reduce the quality of service, e.g., significant transmission delays and frequent packet loss. In an IoV system, transmission time is critically important; messages must be exchanged as fast as possible, particularly in emergency situations. Previous studies have investigated hierarchical techniques to solve these problems. Clustering algorithms may reduce network overhead and guarantee efficient network management. In this paper, we propose an innovative weight-based clustering algorithm that leverages density, speed, position, and delay metrics to ensure quality of service in an IoV system by guaranteeing reliable connectivity and low network overhead. [ABSTRACT FROM AUTHOR]

The IEEE 802.15.4e specified the Time Slotted Channel Hopping (TSCH) that uses multi-channels and shared links to ensure a reliable and efficient data transmission in IoT applications. However, the standard does not define any scheduling mechanism for the network configuration. The main problem in TSCH is triggered when hidden nodes in a shared link transmit data at the same time. A collision happens even if the hidden nodes apply CSMA/CA before starting data transmission. To solve this problem, we propose Interference Collision Free Scheduling (ICFS), and Interference Collision Free Scheduling-Without Redundant Data (ICFS-WRD) algorithms to reduce the internal collisions caused by hidden nodes on shared links. The ICFS-WRD approach stands in contrast to proposals in the recent literature where shared links of the proposed TSCH schedules are free from colliding nodes. ICFS-WRD intentionally schedules the colliding nodes that sense redundant data on the same shared link, and let them alternate in transmitting data. This mechanism is targeted to sparse more cells for future flows, reduce the slot-frame size, increase the network's lifetime and avoid transmitting redundant data. We propose a clustering technique to build a multi-hop cluster based convergecast traffic routing approach with a unique sink, on which we implement the scheduling algorithms. The proposed algorithms have been tested through simulations using network simulator NS3. The results show improvements in terms of energy consumption (ICFS saves approximately 23%), packets delivery ratio (ICFS achieves approximately 96.5%), and latency (ICFS-WRD delivers the packets twice faster than ICFS). We discuss some theorems and proofs that show that ICFS-WRD reduces the slot-frame size, increases the network lifetime, avoids transmitting redundant data, and minimizes the network's congestion. [ABSTRACT FROM AUTHOR]

Summary: In this work, we present a new nature‐inspired cross‐layer routing protocol named ACO‐based MAC‐aware routing protocol (ACOMAR). An enhancement of time division multiple access (TDMA) algorithm is proposed where slots allocation algorithm considers the geographical distance between nodes and the sink. The nodes that are far away from the sink have better chance to be scheduled first. This strategy will result in more efficiency than a usual random TDMA schedule. This algorithm uses a metric based on the pheromone's value of Ant Colony Optimization (ACO) to choose the best next hop with the aim of reducing latency and energy and extending the network lifetime. Comparisons are made between ACOMAR, MAC‐aware routing (MAR) and the latest cross‐layer protocol latency and energy MAC‐aware routing (LEMAR). The experimental results show that ACOMAR outperforms considerably LEMAR by 80%, 20%, 81%, and 54% in latency, energy, hop count, and throughput, respectively. Consequently, ACOMAR is a suitable solution for healthcare applications where WSN‐based critical IoT devices interact quickly in emergence cases (high rate in latency optimization 80%). [ABSTRACT FROM AUTHOR]

With the increasing popularity of Cloud computing systems, the demand for highly dependable Cloud applications has increased significantly. For this, reliability and availability of Cloud applications are two prominent issues for both the providers and the users of Cloud. However, ensuring these two properties in Cloud applications is often very difficult. This is especially because of the characteristics of the Cloud computing paradigm, which is a combination of hardware and software components in a dynamic setting. In spite of the challenges, it is often a key objective to ensure reliability and availability of such applications to guarantee the expected quality of service (QoS). Many methods, strategies and approaches have been proposed in the existing literature; however, as far as we have investigated, these works do not provide a global solution that could provide reliability, availability and high margin of QoS at the same time (for such systems). In this paper, we propose a novel formal framework for constructing reliable and available Cloud components using the DRB (distributed recovery block) scheme. The aim is to provide a strategy that can enhance Cloud dependability through the uniform treatment of software and hardware faults by constructing fault-masking nodes. A fault-masking node is suitable for handling (i.e., detection and tolerance of faults) software, hardware, and response time faults using both the acceptance test and try blocks to ensure safety and liveness properties at the same time. [ABSTRACT FROM AUTHOR]

Global optimization solves real-world problems numerically or analytically by minimizing their objective functions. Most of the analytical algorithms are greedy and computationally intractable (Gonzalez in Handbook of approximation algorithms and metaheuristics: contemporary and emerging applications, vol. 2. CRC Press, Boca Raton, 2018). Metaheuristics are generally nature-inspired optimization algorithms. They numerically find a near-optimal solution for optimization problems in a reasonable amount of time. We propose a novel metaheuristic algorithm for global optimization. It is based on the shooting and jumping behaviors of the archerfish for hunting aerial insects. We name our proposed algorithm the archerfish hunting optimizer (AHO). The AHO algorithm has two parameters (the swapping angle and the attractiveness rate) to set. We execute the AHO algorithm using five different values for each parameter. In all, we perform 25 simulations for four distinct values of the search space dimension (i.e., 5, 10, 15, and 20). We run the Friedman test to determine the best values of parameters for each dimension. We perform three different comparisons to validate the proposed algorithm's performance. First, AHO is compared to 12 recent metaheuristic algorithms (the accepted algorithms for the 2020's competition on single-objective bound-constrained numerical optimization) on ten test functions of the benchmark CEC 2020 for unconstrained optimization. The experimental results are evaluated using the Wilcoxon signed-rank test. Experimental outcomes show that the AHO algorithm, in terms of robustness, convergence, and quality of the obtained solution, is significantly competitive compared to state-of-the-art methods. Second, the performance of AHO and three recent metaheuristic algorithms is evaluated using five engineering design problems taken from the benchmark CEC 2020 for non-convex constrained optimization. The obtained results are ranked using the ranking scheme detailed in the corresponding paper, and the obtained ranks illustrate that AHO is very competitive when opposed to the considered algorithms. Finally, the performance of AHO in solving five engineering design problems is assessed and compared to several well-established state-of-the-art algorithms. We analyzed the obtained numerical results in detail. These results show that the AHO algorithm is significantly better than, or at least comparable to the considered algorithms with very efficient performance in solving many optimization problems. The statistical indicators illustrate that the AHO algorithm has a high ability to significantly outperform the well-established optimizers. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) and the recent advancements in cloud computing have gained importance with the surge in the amount of data generated globally. Moreover, the rapidly increasing applications of the Internet in many scientific and real-time practical applications have ushered in a new era of complex applications of data flow. Tourism and related services are routinely accessed by millions of customers worldwide. Furthermore, with newer, attractive, rapidly growing services, it has become essential for dealers to promote their services using up-to-date technological tools. The major challenge is to efficiently determine and select the best travel options conforming to the needs and financial requirements of the customers. In this study, the use of a dynamic skyline operator for multicriteria decisions is examined using a time-dependent database to select the best services. Moreover, the impact of implementing the operator on optimizing resource consumption is explored. Results indicate that the implementation of this operator is more efficient than the existing techniques. [ABSTRACT FROM AUTHOR]

Recently, Zoom and Microsoft Teams have emerged as the most common tools for lecturing activities in higher education institutions. The fact that these platforms were not originally developed for educational purposes resulted in a significant reduction in learning effectiveness, especially in STEM subjects that require a lot of practical knowledge. Despite the enhancements and updates made to these platforms, they have failed to meet the learning effectiveness criteria. A major hindering to learning effectiveness on such systems can be traced not only to the absence of learning-related features but also to their usability. In this research, we propose a framework for evaluating the usability of online lecturing platforms. The proposed framework introduces a new set of heuristics that are specifically designed for lecturing activities and considers VAK learning styles. Since students with different learning styles learn differently, we developed the first cognitive style-based usability heuristics that can reliably assess the usability of online learning environments for various types of learners. We present a case study that uses the suggested evaluation framework to assess the usability of Zoom and Teams in higher education lecturing activities. The findings of this study, however, can be applied to almost any online lecture platform. [ABSTRACT FROM AUTHOR]

Detecting at-risk students provides advanced benefits for improving student retention rates, effective enrollment management, alumni engagement, targeted marketing improvement, and institutional effectiveness advancement. One of the success factors of educational institutes is based on accurate and timely identification and prioritization of the students requiring assistance. The main objective of this paper is to detect at-risk students as early as possible in order to take appropriate correction measures taking into consideration the most important and influential attributes in students’ data. This paper emphasizes the use of a customized rule-based system (RBS) to identify and visualize at-risk students in early stages throughout the course delivery using the Risk Flag (RF). Moreover, it can serve as a warning tool for instructors to identify those students that may struggle to grasp learning outcomes. The module allows the instructor to have a dashboard that graphically depicts the students’ performance in different coursework components. The at-risk student will be distinguished (flagged), and remedial actions will be communicated to the student, instructor, and stakeholders. The system suggests remedial actions based on the severity of the case and the time the student is flagged. It is expected to improve students’ achievement and success, and it could also have positive impacts on under-performing students, educators, and academic institutions in general. [ABSTRACT FROM AUTHOR]

The merit of higher order functions for hardware description and transformation is widely acknowledged by hardware designers. However, the use of higher order types makes their correctness proof very difficult. Herein, a new proof approach based on the principle of partial application is proposed which transforms higher order functions into partially applied first‐order ones. Therefore, parameterised architectures modelled by higher order functions could be easily redefined only over first‐order types. The proof could be performed by induction within the same specification framework that avoids translating the higher order properties between different semantics, which remains extremely difficult. Using the notion of parameterisation where verified components are used as parameters to build more complex ones, the approach fits elegantly in the incremental bottom‐up design where both the design and its proof could be developed in a systematic way. The potential features of the proposed methodological proof approach are demonstrated over a detailed example of a circuit design and verification within a functional framework. [ABSTRACT FROM AUTHOR]

A novel nature-inspired metaheuristic optimization algorithm, called the quantum firefly algorithm, is proposed in this paper. The algorithm imitates (a) the social behaviour of fireflies mating in nature, (b) laws of quantum physics, and (c) laws of natural evolution. The algorithm combines the powers of two well-known algorithms: the firefly algorithm and the quantum genetic algorithm. The proposed quantum firefly algorithm's performance is tested on 15 mathematical test functions and one structural design problem. The obtained results show that the quantum firefly algorithm is very competitive compared to the firefly algorithm and the quantum genetic algorithm. [ABSTRACT FROM AUTHOR]

As a new area of technology, the Internet of Things (IoT) is a flagship and promising paradigm for innovating society. However, IoT-based critical infrastructures are an appealing target for cybercriminals. Such distinctive infrastructures are increasingly sensitive to cyber vulnerabilities and subject to many cyberattacks. Thus, protecting these infrastructures is a significant issue for organizations and nations. In this context, raising the cybersecurity posture of critical cyber infrastructures is an extremely urgent international issue. In addition, with the rapid development of adversarial techniques, current cyber threats have become more sophisticated, complicated, advanced and persistent. Thus, given these factors, prior to implementing efficient and resilient cybersecurity countermeasures, identification and in-depth mapping of cyber threats is an important step that is generally overlooked. Therefore, to solve cybersecurity challenges, this study presents a critical analysis of the most recent cybersecurity issues for IoT-based critical infrastructures. We then discuss potential cyber threats and cyber vulnerabilities and the main exploitation strategies adopted by cybercriminals. Further, we provide a taxonomy of cyberattacks that may affect critical cyber infrastructures. Finally, we present security requirements and some realistic recommendations to enhance cybersecurity solutions. [ABSTRACT FROM AUTHOR]

The rapid deployment of IoT in different areas generates a massive amount of data transferred to the Cloud. To solve this challenge a new paradigm, called Fog Computing, is located at the edge of the network and close to the connected objects. Its main role is to extend the capacities of Cloud and improve the performance and the QoS required by the applications by the use of different methods and techniques based on scheduling algorithms. In this paper, we review various recent studies available in the literature that are interested in the scheduling methods and algorithms used in Fog computing. The use of fog layer, in solving optimization problem, is faced with serious challenges. Therefore, to help practitioners and researchers, we present an in-depth overview of Fog Computing studying various scheduling methods and algorithms. We analyze, compare and classify these different scheduling approaches according to the nature of the algorithm used in the scheduling, the QoS optimized by the proposed approach and the type of applications in order to show what is suitable for critical IoT (CIOT), massive IoT (MIOT) and Industry IoT (IIOT). Finally, we present a comparison of the different simulation tools used to evaluate these approaches to guide fog computing developers/researchers which tool is suitable and most flexible for simulating the application under consideration. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new Distributed Clustering Algorithm Based on Dominating Set (DS) for Internet of Vehicles, called DCA-DS. To construct the DS, DCA-DS algorithm introduces a new parameter, called node span, which represents the number of the node neighbours that are not unclustered, including the node itself. DCA-DS algorithm is based on a simple heuristic method that uses a greedy strategy, where the node having the largest span is included in the DS, therefore it acts as new CH and all its neighbours become Cluster Members (CMs). This process repeats iteratively until there are no unclustered nodes left. Moreover, the node, which can hear two CHs or more, will act as Cluster Gateway (CG). Furthermore, DCA-DS algorithm takes care of the maintenance phase to keep clusters stability and structure. The proposed approach is implemented in NS-2 network simulator and VanetMobiSim mobility simulator to evaluate its performance. [ABSTRACT FROM AUTHOR]

The main problem facing the Internet of Things (IoT) today is the identification of attacks due to the constrained nature of IoT devices. To address this problem, we present a lightweight intrusion detection system (IDS) which acts as a second line of defense allowing the reinforcement of the access control mechanism. The proposed method is based on a Deep Auto-Encoder (DAE), which learns the pattern of a normal process using only the features of the user's normal behavior. Whatever deviation from the expected behavior is considered an anomaly. We validate our approach using two well-known network datasets, namely, the NSL-KDD and CIDDS-001. The experimental results demonstrate that our approach provides promising results in terms of accuracy, detection rate and false alarm rate. [ABSTRACT FROM AUTHOR]

Summary: Vehicular ad hoc networks (VANETs) have recently attracted considerable attention owing to their wide range of applications. However, there are several challenges, such as mobility, routing, scalability, quality of services, and security. Clustering is an important control mechanism in high‐mobility networks and has been verified to be a promising approach in VANETs as well, as it ensures a basic level of network performance. Accordingly, several clustering algorithms have been proposed for these networks, and different protocols typically focus on various performance metrics. In this study, we provide a thorough review of clustering algorithms in VANETs. First, we present background material regarding the clustering process. Secondly, we propose a new taxonomy that categorizes clustering algorithms in VANETs based on different design aspects and provides a description of the algorithms in each category. Thirdly, an analysis of the algorithms in each category is provided according to various comparison metrics. Fourthly, we highlight the main challenges for each category and discuss some open research issues. Finally, we provide a general comparison of different clustering algorithms according to selected key parameters. Thus, this study provides a more thorough understanding of VANET clustering algorithms and the research trends in this area. [ABSTRACT FROM AUTHOR]

Vehicular ad hoc networks (VANETs) have recently gained a lot of interest. This is due to their capacity to improve driving safety and traffic management. VANET is designed to provide emerging applications and services to drivers in vehicular environments. On the one hand, the rapid advancement of automobile industry enables the development of vehicles with various forms of onboard computation facilities, storage, communication and sensing resources. On the other hand, the application of cloud computing (CC) affects several domains, including the transportation domain, therefore the emergence of vehicular cloud computing (VCC). VCC is an efficient technology to move VANET toward autonomous driving, vehicle control, and intelligent systems. Security and privacy issues are among the major concerns that should be addressed in the context of VCC. However, the special characteristics of VCC have made the security used in classical VANET and CC to be revised. To the best of our knowledge, there is a lack of security surveys for VCC. This paper is an attempt to discuss the challenges faced by security for VCC and future guide to handling these challenges. [ABSTRACT FROM AUTHOR]

Information wikis and especially Wikipedia have become one of the most attractive environments for informal learning. The nature of wikis enables learners to freely navigate the learning environment and independently construct knowledge without being required to follow a predefined learning path in line with the constructivist learning theory. Link-based navigation and keyword-based search methods used on Wikipedia and similar information wikis suffer from many limitations. In our paper, we present an effective recommendation system that provides easier and faster access to relevant content on Wikipedia to support informal learning. In addition, we evaluate the impact of personalized content recommendations on informal learning from Wikipedia and show how web analytics data can be used to get an insight on informal learning in similar environments. [ABSTRACT FROM AUTHOR]

This paper tackles the Multi-Robot Task Allocation problem. It consists of two distinct sets: a set of tasks (requiring resources), and a set of robots (offering resources). Then, the tasks are allocated to robots while optimizing a certain objective function subject to some constraints; e.g., allocating the maximum number of tasks, minimizing the distances traveled by the robots, etc. Previous works mainly optimized the temporal and spatial constraints, but no work focused on energetic constraints. Our main contribution is the introduction of energetic constraints on multi-robot task allocation problems. In addition, we propose an allocation method based on parallel distributed guided genetic algorithms and compare it to two state-of-the-art algorithms. The performed simulations and obtained results show the effectiveness and scalability of our solution, even in the case of a large number of robots and tasks. We believe that our contribution is applicable in many contemporary areas of research such as smart cities and related topics. [ABSTRACT FROM AUTHOR]

The emergence of the Internet of Things has led to the evolution of conventional Vehicular Ad-hoc NETworks (VANETs) toward a new paradigm called the Internet of Vehicles (IoV). This latter has recently received the attention of many researchers. In order to overcome the different challenges, such as high mobility, the number of connected vehicles and the big data, clustering, as an important technique used in a VANET, has significantly improved the performance of numerous applications. In this regard, the present work proposes a new heuristic clustering algorithm based on RSU called HCAR for IoV, which entails the centralization of a clustering algorithm at distributed RSUs. This latter is responsible for performing the cluster formation phase based on a simple heuristic algorithm, using graph theory concepts, such as node degree and adjacency matrix. In addition, HCAR uses a new mechanism to recover the problem of the unavailability of cluster head (CH), through the election of a secondary CH using a weighted mechanism. Moreover, HCAR takes care of the maintenance phase to keep clusters' stability and structure. The efficiency of the proposed scheme is proven theoretically, and the simulation results, in the NS-2 and VanetMobiSim integrated environment, show its performance advantages. [ABSTRACT FROM AUTHOR]

The problem of task allocation in a multi-robot system is the situation where we have a set of tasks and a number of robots; then each task is assigned to the appropriate robots with the aim of optimizing some criteria subject to constraints, e.g., allocate the maximum number of tasks. We propose an effective solution to address this problem. It implements a two-stage methodology: first, a global allocation based of the well-known firefly algorithm, and then, a local allocation combining advantages of quantum genetic algorithms and artificial bee colony optimization. We compared our proposed solution to one solution from the state of the art. The simulation results show that our scheme significantly performs better than this solution. Our solution allocated 100 % of the tasks (in every configuration tried in the experiments) and enhanced the allocation time by 75 % . [ABSTRACT FROM AUTHOR]

MAC Protocols enables sensor nodes of the same WSN to access a common shared communication channel. Many researchers have proposed different solutions explaining how to design and implement these protocols. The main goal of most MACs protocols is how to prolong lifetime of the WSN as long as possible by reducing energy consumption since it is often impossible to change or to recharge sensors' batteries. The majority of these protocols designed for WSN are based on "duty-cycle" technique. Every node of the WSN operates on two periods: active period and sleep period to save energy. Until now (to our knowledge) there is no ideal protocol for this purpose. The main reason relies on the lack of standardization at lower layers (physical layer) and (physical) sensor hardware. Therefore, the MAC protocol choice remains application-dependent. A useful MAC protocol should be able to adapt to network changes (topology, nodes density and network size). This paper surveys MAC protocols for WSNs and discusses the main characteristics, advantages and disadvantages of currently popular protocols. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has drawn significant attention in recent years since it has made revolutionary changes in human life. The IoT enables the exchange of information in a wide variety of applications such as smart buildings, smart health, smart transport, and so on. These diverse application domains can be unified into a single entity referred as smart life. The rapid evolution of the IoT has pushed a race between cyber-criminals and security experts. As billions of connected things communicate with each other and can exchange sensitive information that may be leaked. Hence, strengthening IoT's security and preserving users' privacy is a major challenge. This paper aims to provide a comprehensive study of the IoT security. Several IoT security attacks are analyzed, and a taxonomy of the security requirements based on the attacks' purposes is proposed. Moreover, recent security solutions are described and classified based on their application domains. Finally, open research directions and security challenges are discussed. [ABSTRACT FROM AUTHOR]

The dramatic growth of HTTP adaptive streaming (HAS) traffic represents a practical challenge for service providers in satisfying the demand from their customers. Achieving this in a network where multiple players share the network capacity has so far proved hard because of the bandwidth competition among the HAS players. This competition is exacerbated by the bandwidth overestimation that is introduced due to the isolated and selfish behavior of theHAS players. Each player strives individually to select themaximum bitratewithout considering the co-existing players or network resource dynamics. As a result, the HAS players suffer from video quality instability, quality unfairness, and network underutilization or oversubscription, and the players observe a poor quality of experience (QoE). To address this issue, we propose a fully distributed game theory and consensus-based collaborative adaptive bitrate solution for shared network environments, termed Game Theory and consensus-based Approach for Cooperative HAS delivery systems (GTAC). Our solution consists of two-stage games that run in parallel during a streaming session.We extensively evaluate GTAC on a broad set of trace-driven and real-world experiments. Results show that GTAC enhances the viewer QoE by up to 22%, presentation quality stability by up to 24%, fairness by at least 31%, and network utilization by 28% compared to the well-known schemes. [ABSTRACT FROM AUTHOR]

A plethora of infinite data is generated from the Internet and other information sources. Analyzing this massive data in real-time and extracting valuable knowledge using different mining applications platforms have been an area for research and industry as well. However, data stream mining has different challenges making it different from traditional data mining. Recently, many studies have addressed the concerns on massive data mining problems and proposed several techniques that produce impressive results. In this paper, we review real time clustering and classification mining techniques for data stream. We analyze the characteristics of data stream mining and discuss the challenges and research issues of data steam mining. Finally, we present some of the platforms for data stream mining. [ABSTRACT FROM AUTHOR]

Purpose This paper is a review of a number routing protocols in the internet of vehicles (IoV). IoV emphasizes information interaction among humans, vehicles and a roadside unit (RSU), within which routing is one of the most important steps in IoV network.Design/methodology/approach In this paper, the authors have summarized different research data on routing protocols in the IoV. Several routing protocols for IoV have been proposed in the literature. Their classification is made according to some criteria such as topology-based, position-based, transmission strategy and network structure. This paper focuses on the transmission strategy criteria. There exist three types of protocols that are based on this strategy: unicast protocol, broadcast protocols and multicast protocols. This later type is classified into two subclasses: geocast and cluster-based protocols. The taxonomy of the transmission strategy is presented in this study. We discuss the advantages and disadvantages of each type with a general comparison between the five types.Findings The authors can deduce that many challenges are encountered when designing routing protocols for IoV.Originality/value A simple and well-explained presentation of the functioning of the IoV is provided with a comparison among each categories of protocols is well presented along with the advantages and disadvantages of each type. The authors examined the main problems encountered during the design of IoV routing protocol, such as the quick change of topology, the frequent disconnection, the big volume of data to be processed and stored in the IoV, and the problem of network fragmentation. This work explores, compares existing routing protocols in IoV and provides a critical analysis. For that, the authors extract the challenges and propose future perspectives for each categories of protocols. [ABSTRACT FROM AUTHOR]

Due to the huge potential profit that sensor networks may provide in industrial and socio-economic areas, they attract the attention of many researchers around the world. However, their development level has not reached an acceptable status due to several barriers. Each sensor is equipped with a battery but of limited energy which causes major obstacle on the lifetime of the network. These sensors have communicated with the sink which might be far away. A mobile sink can be considered as an alternative solution to the problems already identified, but it also raises new challenges such as the location of the mobile sink and communication with the sensors. In this paper, we proposed a new robust routing protocol called Tree and Mobile Sink-based Routing Protocol. This protocol is based on the intra-cluster multi-hop concept using a constructed tree for data routing and a new strategy for sink mobility to collect data from cluster heads. In order to evaluate the performance of algorithm, we implemented it using the NS2 network simulator. [ABSTRACT FROM AUTHOR]

The emergence of Internet of Things (IoT) is empowered by the availability of the high volume of smart sensors, Radio Frequency Identification, a suitable communication technologies and protocols. In the near future, the Internet will be full of heterogeneous connected devices. In recent years, the IoT has drawn significant attention as it can solve difficult problems. However, the heterogeneity of devices and the large scale networks expose the IoT to many challenges that must be addressed; otherwise, the systems performance will deteriorate. As an attempt to identify these challenges, this paper comprehensibly cites the main IoT concepts, the serious IoT challenges and the quality of services presented in the recent literature. It also investigates the corresponding main research directions and the proposed solutions. This paper can increase the knowledge of the reader since it is the first IoT survey that presents load balancing algorithms utilized in solving the extreme data storage challenge. [ABSTRACT FROM AUTHOR]