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The next generation of wireless networks, 6G is being designed with data-intensive applications. One of the key technologies that will enable 6G is blockchain technology. The emergence of blockchain technology and 6G networks has revolutionized service migration. Service migration in 6G networks is a complex process that requires the integration of new technologies, such as artificial intelligence (AI), edge computing, and network slicing. Motivated by these facts, this comprehensive review includes an overview of blockchain and service migration integration in 6G. First, state of art, development frame work and related works were introduced. Then, we used content analysis by WordStat software and bibliographic analysis by VOSviewer to analysis the current status of service migration and blockchain integration in 6G networks. Next, patterns and characteristics, benefits and challenges and potential cases were reviewed. Then, we proposed an architectural blockchain-based model including decentralized architecture, edge computing, network slicing, softwaredefined networking, and 5G-6G interworking in 6G. Finally, we described potential application service migration-based in 6G networks including digital twin (DT), holograms, robot avatar, high density internet of things (IoT), AR and VR in 6G and collected open research and future directions of service migration and blockchain. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has brought about significant transformations in multiple sectors, including healthcare and navigation systems, by offering essential functionalities crucial for their operations. Nevertheless, there is ongoing debate surrounding the unexplored possibilities of the IoT within the energy industry. The requirement to better the performance of distributed energy systems necessitates transitioning from traditional mission-critical electric smart grid systems to digital twin-based IoT frameworks. Energy storage systems (ESSs) used within nano-grids have the potential to enhance energy utilization, fortify resilience, and promote sustainable practices by effectively storing surplus energy. The present study introduces a conceptual framework consisting of two fundamental modules: (1) Power optimization of energy storage systems (ESSs) in peer-to-peer (P2P) energy trading. (2) Task orchestration in IoT-enabled environments using digital twin technology. The optimization of energy storage systems (ESSs) aims to effectively manage surplus ESS energy by employing particle swarm optimization (PSO) techniques. This approach is designed to fulfill the energy needs of the ESS itself as well as meet the specific requirements of participating nano-grids. The primary objective of the IoT task orchestration system, which is based on the concept of digital twins, is to enhance the process of peer-to-peer nano-grid energy trading. This is achieved by integrating virtual control mechanisms through orchestration technology combining task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. The nano-grid energy trading system's architecture utilizes IoT sensors and Raspberry Pi-based edge technology to enable virtual operation. The evaluation of the proposed study is carried out through the examination of a simulated dataset derived from nano-grid dwellings. This research analyzes the efficacy of optimization approaches in mitigating energy trading costs and optimizing power utilization in energy storage systems (ESSs). The coordination of IoT devices is crucial in improving the system's overall efficiency. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging technology that has recently gained significant interest, especially with the dramatic increase in connected devices. However, IoT networks are not yet standardized, and the design of such networks faces many challenges, including scalability, flexibility, reliability, and availability of such networks. Routing is among the significant problems facing IoT network design because of the dramatic increase in connected devices and the network requirements regarding availability, reliability, latency, and flexibility. To this end, this work investigates deploying a multipath routing scheme for dense IoT networks. The proposed method selects a group of routes from all available routes to forward data at a maximum rate. The choice of data transmission routes is a complex problem for which numerical optimization methods can be used. A novel method for selecting the optimum group of routes and coefficients of traffic distribution along them is proposed. The proposed method is implemented using dynamic programming. The proposed method outperforms the traditional route selection methods, e.g., random route selection, especially for dense IoT networks. The model significantly reduced the number of intermediate nodes involved in routing paths over dense IoT networks by 34%. Moreover, it effectively demonstrated a significant decrease of 52% in communication overhead and 40% in data delivery time in dense IoT networks compared to traditional models. [ABSTRACT FROM AUTHOR]

Breast cancer, a leading cause of female mortality worldwide, poses a significant health challenge. Recent advancements in deep learning techniques have revolutionized breast cancer pathology by enabling accurate image classification. Various imaging methods, such as mammography, CT, MRI, ultrasound, and biopsies, aid in breast cancer detection. Computer-assisted pathological image classification is of paramount importance for breast cancer diagnosis. This study introduces a novel approach to breast cancer histopathological image classification. It leverages modified pre-trained CNN models and attention mechanisms to enhance model interpretability and robustness, emphasizing localized features and enabling accurate discrimination of complex cases. Our method involves transfer learning with deep CNN models—Xception, VGG16, ResNet50, MobileNet, and DenseNet121—augmented with the convolutional block attention module (CBAM). The pre-trained models are finetuned, and the two CBAM models are incorporated at the end of the pre-trained models. The models are compared to state-of-the-art breast cancer diagnosis approaches and tested for accuracy, precision, recall, and F1 score. The confusion matrices are used to evaluate and visualize the results of the compared models. They help in assessing the models' performance. The test accuracy rates for the attention mechanism (AM) using the Xception model on the "BreakHis" breast cancer dataset are encouraging at 99.2% and 99.5%. The test accuracy for DenseNet121 with AMs is 99.6%. The proposed approaches also performed better than previous approaches examined in the related studies. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle (UAV) networks offer a wide range of applications in an overload situation, broadcasting and advertising, public safety, disaster management, etc. Providing robust communication services to mobile users (MUs) is a challenging task because of the dynamic characteristics of MUs. Resource allocation, including subchannels, transmit power, and serving users, is a critical transmission problem; further, it is also crucial to improve the coverage and energy efficacy of UAV-assisted transmission networks. This paper presents an Enhanced Slime Mould Optimization with Deep-Learning-based Resource Allocation Approach (ESMOML-RAA) in UAV-enabled wireless networks. The presented ESMOML-RAA technique aims to efficiently accomplish computationally and energy-effective decisions. In addition, the ESMOML-RAA technique considers a UAV as a learning agent with the formation of a resource assignment decision as an action and designs a reward function with the intention of the minimization of the weighted resource consumption. For resource allocation, the presented ESMOML-RAA technique employs a highly parallelized long short-term memory (HP-LSTM) model with an ESMO algorithm as a hyperparameter optimizer. Using the ESMO algorithm helps properly tune the hyperparameters related to the HP-LSTM model. The performance validation of the ESMOML-RAA technique is tested using a series of simulations. This comparison study reports the enhanced performance of the ESMOML-RAA technique over other ML models. [ABSTRACT FROM AUTHOR]

This study uses deep learning (DL) techniques for pilot-based channel estimation in orthogonal frequency division multiplexing (OFDM). Conventional channel estimators in pilot-symbol-aided OFDM systems suffer from performance degradation, especially in low signal-to-noise ratio (SNR) regions, due to noise amplification in the estimation process, intercarrier interference, a lack of primary channel data, and poor performance with few pilots, although they exhibit lower complexity and require implicit knowledge of the channel statistics. A new method for estimating channels using DL with peephole long short-term memory (peephole LSTM) is proposed. The proposed peephole LSTM-based channel state estimator is deployed online after offline training with generated datasets to track channel parameters, which enables robust recovery of transmitted data. A comparison is made between the proposed estimator and conventional LSTM and GRU-based channel state estimators using three different DL optimization techniques. Due to the outstanding learning and generalization properties of the DL-based peephole LSTM model, the suggested estimator significantly outperforms the conventional least square (LS) and minimum mean square error (MMSE) estimators, especially with a few pilots. The suggested estimator can be used without prior information on channel statistics. For this reason, it seems promising that the proposed estimator can be used to estimate the channel states of an OFDM communication system. [ABSTRACT FROM AUTHOR]

The safeguarding of online services and prevention of unauthorized access by hackers rely heavily on user authentication, which is considered a crucial aspect of security. Currently, multi-factor authentication is used by enterprises to enhance security by integrating multiple verification methods rather than relying on a single method of authentication, which is considered less secure. Keystroke dynamics is a behavioral characteristic used to evaluate an individual's typing patterns to verify their legitimacy. This technique is preferred because the acquisition of such data is a simple process that does not require any additional user effort or equipment during the authentication process. This study proposes an optimized convolutional neural network that is designed to extract improved features by utilizing data synthesization and quantile transformation to maximize results. Additionally, an ensemble learning technique is used as the main algorithm for the training and testing phases. A publicly available benchmark dataset from Carnegie Mellon University (CMU) was utilized to evaluate the proposed method, achieving an average accuracy of 99.95%, an average equal error rate (EER) of 0.65%, and an average area under the curve (AUC) of 99.99%, surpassing recent advancements made on the CMU dataset. [ABSTRACT FROM AUTHOR]

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeterwave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration technique for complex edge computing structures. The developed migration scheme enables services to adapt to the required load on the radio channels. The proposed algorithm is analyzed for various use cases, and a test bench has been developed to emulate the operator's infrastructure. The obtained results are introduced and discussed. [ABSTRACT FROM AUTHOR]

Next-generation networks, including the Internet of Things (IoT), fifthgeneration cellular systems (5G), and sixth-generation cellular systems (6G), suffer from the dramatic increase of the number of deployed devices. This puts high constraints and challenges on the design of such networks. Structural changing of the network is one of such challenges that affect the network performance, including the required quality of service (QoS). The fractal dimension (FD) is considered one of the main indicators used to represent the structure of the communication network. To this end, this work analyzes the FD of the network and its use for telecommunication networks investigation and planning. The cluster growing method for assessing the FD is introduced and analyzed. The article proposes a novel method for estimating the FD of a communication network, based on assessing the network's connectivity, by searching for the shortest routes. Unlike the cluster growing method, the proposed method does not require multiple iterations, which reduces the number of calculations, and increases the stability of the results obtained. Thus, the proposed method requires less computational cost than the cluster growing method and achieves higher stability. The method is quite simple to implement and can be used in the tasks of research and planning of modern and promising communication networks. The developed method is evaluated for two different network structures and compared with the cluster growing method. Results validate the developed method. [ABSTRACT FROM AUTHOR]

In this paper, the novel study of an Internet of Things (IoT) network model with multimodal node distribution and a data-collecting mechanism using mobile clustering nodes is presented. The aim of this work is to introduce the problem of organizing the mobile cluster head IoT network with a heterogeneous distribution node in the service area with multimodal distribution nodes. A new method for clustering a heterogeneous network is proposed, which makes it possible to efficiently identify clusters that differ in terms of the density of nodes. This makes it possible to choose the speed of the mobile cluster head in accordance with the density in each cluster. The proposed method uses the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering algorithm. One of the benefits of our proposed model is the increase in the efficiency of using a mobile cluster head. The new solution can be used to organize data collection in the IoT. [ABSTRACT FROM AUTHOR]

In smart cities, target detection is one of the major issues in order to avoid traffic congestion. It is also one of the key topics for military, traffic, civilian, sports, and numerous other applications. In daily life, target detection is one of the challenging and serious tasks in traffic congestion due to various factors such as background motion, small recipient size, unclear object characteristics, and drastic occlusion. For target examination, unmanned aerial vehicles (UAVs) are becoming an engaging solution due to their mobility, low cost, wide field of view, accessibility of trained manipulators, a low threat to people's lives, and ease to use. Because of these benefits along with good tracking effectiveness and resolution, UAVs have received much attention in transportation technology for tracking and analyzing targets. However, objects in UAV images are usually small, so after a neural estimation, a large quantity of detailed knowledge about the objects may be missed, which results in a deficient performance of actual recognition models. To tackle these issues, many deep learning (DL)-based approaches have been proposed. In this review paper, we study an end-to-end target detection paradigm based on different DL approaches, which includes one-stage and two-stage detectors from UAV images to observe the target in traffic congestion under complex circumstances. Moreover, we also analyze the evaluation work to enhance the accuracy, reduce the computational cost, and optimize the design. Furthermore, we also provided the comparison and differences of various technologies for target detection followed by future research trends. [ABSTRACT FROM AUTHOR]

This article presents a new network model for IoT that is based on a multi-story building structure. The model locates network nodes in a regular, cubic lattice-like structure, resulting in an equation for the signal-to-noise ratio (SNR). The study also determines the relationship between traffic density, network density, and SNR. In addition, the article explores the potential of percolation theory in characterizing network functionality. The findings offer a new approach to network design and planning, allowing for selecting a network topology that meets criteria and requirements while ensuring connectivity and improving efficiency. The developed analytical apparatus provides valuable insights into the properties of the network and its applicability to specific conditions. [ABSTRACT FROM AUTHOR]

Diabetes is a chronic disease that is escalating day by day and requires 24/7 continuous management. It may cause many complications, precisely when a patient moves, which may risk their and other drivers' and pedestrians' lives. Recent research shows diabetic drivers are the main cause of major road accidents. Several wireless non-invasive health monitoring sensors, such as wearable continuous glucose monitoring (CGM) sensors, in combination with machine learning approaches at cloud servers, can be beneficial for monitoring drivers' diabetic conditions on travel to reduce the accident rate. Furthermore, the emergency condition of the driver needs to be shared for the safety of life. With the emergence of the vehicular ad-hoc network (VANET), vehicles can exchange useful information with nearby vehicles and roadside units that can be further communicated with health monitoring sources via GPS and Internet connectivity. This work proposes a novel approach to the health care of drivers' diabetes monitoring using wearable sensors, machine learning, and VANET technology. Several machine learning (ML) algorithms assessed the proposed prediction model using the cross-validation method. Performance metrics precision, recall, accuracy, F1-score, sensitivity, specificity, MCC, and AROC are used to validate our method. The result shows random forest (RF) outperforms and achieves the highest accuracy compared to other algorithms and previous approaches ranging from 90.3% to 99.5%. [ABSTRACT FROM AUTHOR]

Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of the critical issues found in the VANET environment, with the ability to communicate and enhance the mechanism to enlarge the field. The vehicles are attacked by malicious nodes, especially DDoS attack detection. Several solutions are presented to overcome the issue, but none are solved in a real-time scenario using machine learning. During DDoS attacks, multiple vehicles are used in the attack as a flood on the targeted vehicle, so communication packets are not received, and replies to requests do not correspond in this regard. In this research, we selected the problem of malicious node detection and proposed a real-time malicious node detection system using machine learning. We proposed a distributed multi-layer classifier and evaluated the results using OMNET++ and SUMO with machine learning classification using GBT, LR, MLPC, RF, and SVM models. The group of normal vehicles and attacking vehicles dataset is considered to apply the proposed model. The simulation results effectively enhance the attack classification with an accuracy of 99%. Under LR and SVM, the system achieved 94 and 97%, respectively. The RF and GBT achieved better performance with 98% and 97% accuracy values, respectively. Since we have adopted Amazon Web Services, the network's performance has improved because training and testing time do not increase when we include more nodes in the network. [ABSTRACT FROM AUTHOR]

Telecommunication networks are growing exponentially due to their significant role in civilization and industry. As a result of this very significant role, diverse applications have been appeared, which require secured links for data transmission. However, Internet-of-Things (IoT) devices are a substantial field that utilizes the wireless communication infrastructure. However, the IoT, besides the diversity of communications, are more vulnerable to attacks due to the physical distribution in real world. Attackers may prevent the services from running or even forward all of the critical data across the network. That is, an Intrusion Detection System (IDS) has to be integrated into the communication networks. In the literature, there are numerous methodologies to implement the IDSs. In this paper, two distinct models are proposed. In the first model, a custom Convolutional Neural Network (CNN) was constructed and combined with Long Short Term Memory (LSTM) deep network layers. The second model was built about the all fully connected layers (dense layers) to construct an Artificial Neural Network (ANN). Thus, the second model, which is a custom of an ANN layers with various dimensions, is proposed. Results were outstanding a compared to the Logistic Regression algorithm (LR), where an accuracy of 97.01% was obtained in the second model and 96.08% in the first model, compared to the LR algorithm, which showed an accuracy of 92.8%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a recent technology, which implies the union of objects, “things”, into a single worldwide network. This promising paradigm faces many design challenges associated with the dramatic increase in the number of end-devices. Device identification is one of these challenges that becomes complicated with the increase of network devices. Despite this, there is still no universally accepted method of identifying things that would satisfy all requirements of the existing IoT devices and applications. In this regard, one of the most important problems is choosing an identification system for all IoT devices connected to the public communication networks. Many unique software and hardware solutions are used as a unique global identifier; however, such solutions have many limitations. This article proposes a novel solution, based on the Digital Object Architecture (DOA), that meets the requirements of identifying devices and applications of the IoT. This work analyzes the benefits of using the DOA as an identification platform in modern telecommunication networks. We propose a model of an identification system based on the architecture of digital objects, which differs from the well-known ones. The proposed model ensures an acceptable quality of service (QoS) in the common architecture of the existing public communication networks. A novel interaction architecture is developed by introducing a Middle Handle Register (MHR) between the global register, i.e., Global Handle Register (GHR), and local register, i.e., Local Handle Register (LHR). The aspects of the network interaction and the compatibility of IoT end-devices with the integrated DOA identifiers in heterogeneous communication networks are presented. The developed model is simulated for a wide-area network with allocated registers, and the results are introduced and discussed. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has radically transformed how patient information and healthcare monitoring are monitored and recorded and has revolutionized the area by ensuring regular 24 × 7 tracking without costly and restricted human resources and with a low mistake probability. The Internet of Medical Things (IoMT) is a subsection of the Internet of things (IoT) that uses medical equipment as things or nodes to enable cost-effective and efficient patient monitoring and recording. The IoMT can cope with a wide range of problems, including observing patients in hospitals, monitoring patients in their homes, and assisting consulting physicians and nurses in monitoring health conditions at regular intervals and issuing warning signals if emergency care is necessary. EEG signals, electrocardiograms (ECGs), blood sugar levels, blood pressure levels, and other conditions can be examined. In crucial situations, quick and real-time analysis is essential, and failure to provide careful attention can be fatal. A cloud-based IoT platform cannot handle these latency-sensitive conditions. Fog computing (FC) is a novel paradigm for assigning, processing, and storing resources to IoT devices with limited resources. Where substantial processing power or storage is required, all nodes in a fog computing scheme can delegate their jobs to local fog nodes rather than forwarding them to the cloud module at a greater distance. Identifying potential security risks and putting in place adequate security measures are critical. This work aims to examine a blockchain (BC) as a potential tool for mitigating the impact of these difficulties in conjunction with fog computing. This research shows that blockchain can overcome fog computing's privacy and security concerns. It also discusses blockchain's issues and limitations from the perspective of fog computing (FC) and the IoMT. [ABSTRACT FROM AUTHOR]

In recent years, autonomous vehicles have become more and more popular due to their broad influence over society, as they increase passenger safety and convenience, lower fuel consumption, reduce traffic blockage and accidents, save costs, and enhance reliability. However, autonomous vehicles suffer from some functionality errors which need to be minimized before they are completely deployed onto main roads. Pedestrian detection is one of the most considerable tasks (functionality errors) in autonomous vehicles to prevent accidents. However, accurate pedestrian detection is a very challenging task due to the following issues: (i) occlusion and deformation and (ii) low-quality and multi-spectral images. Recently, deep learning (DL) technologies have exhibited great potential for addressing the aforementioned pedestrian detection issues in autonomous vehicles. This survey paper provides an overview of pedestrian detection issues and the recent advances made in addressing them with the help of DL techniques. Informative discussions and future research works are also presented, with the aim of offering insights to the readers and motivating new research directions. [ABSTRACT FROM AUTHOR]

The Intrusion Detection System (IDS) is an important feature that should be integrated in high density sensor networks, particularly in wireless sensor networks (WSNs). Dynamic routing information communication and an unprotected public media make them easy targets for a wide variety of security threats. IDSs are helpful tools that can detect and prevent system vulnerabilities in a network. Unfortunately, there is no possibility to construct advanced protective measures within the basic infrastructure of the WSN. There seem to be a variety of machine learning (ML) approaches that are used to combat the infiltration issues plaguing WSNs. The Slime Mould Algorithm (SMA) is a recently suggested ML approach for optimization problems. Therefore, in this paper, SMA will be integrated into an IDS for WSN for anomaly detection. The SMA's role is to reduce the number of features in the dataset from 41 to five features. The classification was accomplished by two methods, Support Vector Machine with polynomial core and decision tree. The SMA showed comparable results based on the NSL-KDD dataset, where 99.39%, 0.61%, 99.36%, 99.42%, 99.33%, 0.58%, and 99.34%, corresponding to accuracy, error rate, sensitivity, specificity, precision, false positive rate, and F-measure, respectively, are obtained, which are significantly improved values when compared to other works. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) is capable of addressing the complexities and difficulties of fifth-generation (5G) mobile networks and beyond. In this paradigm, it is important to predict network metrics to meet future network requirements. Vehicle-to-everything (V2X) networks are promising wireless communication methods where traffic information exchange in an intelligent transportation system (ITS) still faces challenges, such as V2X communication congestion when many vehicles suddenly appear in an area. In this paper, a deep learning algorithm (DL) based on the unidirectional long short-term memory (LSTM) model is proposed to predict traffic in V2X networks. The prediction problems are studied in different cases depending on the number of packets sent per second. The prediction accuracy is measured in terms of root-mean-square error (RMSE), mean absolute percentage error (MAPE), and processing time. [ABSTRACT FROM AUTHOR]

This paper presents a generalized approach to the performance analysis of relay-aided communication systems for 5G-and-beyond scenarios. A dual-hop decoding and forwarding scheme is considered in the analysis. The relationship between the outage performance and cumulative distribution function (CDF) of the signal-to-noise ratio (SNR) is exploited to derive a universal expression of the outage probability that is valid for all fading scenarios, irrespective of their nature or complexity. Furthermore, an effort is made to parameterize the channel PDF in such a manner that reflects a practical fading scenario that is commonly encountered in current and future wireless communication systems. The analytical results obtained for various cases are validated through Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

Reversible Data Hiding (RDH) should be secured as per requirements to protect content in open environments such as the cloud and internet. Integrity and undetectability of steganographic images are amongst the main concerns in any RDH scheme. As steganographic encryption using linear prediction over bit-planes is challenging, so the security and embedding capacity of the existing RDH techniques could not be adequate. Therefore, a new steganographic technique is proposed which provides better security, higher embedding capacity and visual quality to the RDH scheme. In this technique, the cover image is divided into n-bit planes (nBPs) and linear prediction is applied to it. Next, the histogram of the residual nBPs image is taken, and secret data bits are encrypted using the RC4 cryptographic algorithm. To embed the encrypted secret data bits, the histogram shifting process is applied. This is achieved by using peak and zero pairs of residual nBPs images. This scheme provides security to the cover image and hidden data. The proposed RDH scheme is capable of extracting the embedded secret data accurately and recovering the original cover or residual nBPs image. [ABSTRACT FROM AUTHOR]

An electrocardiogram (ECG) is an essential piece of medical equipment that helps diagnose various heart-related conditions in patients. An automated diagnostic tool is required to detect significant episodes in long-term ECG records. It is a very challenging task for cardiologists to analyze long-term ECG records in a short time. Therefore, a computer-based diagnosis tool is required to identify crucial episodes. Myocardial infarction (MI) and conduction disorders (CDs), sometimes known as heart blocks, are medical diseases that occur when a coronary artery becomes fully or suddenly stopped or when blood flow in these arteries slows dramatically. As a result, several researchers have utilized deep learning methods for MI and CD detection. However, there are one or more of the following challenges when using deep learning algorithms: (i) struggles with real-life data, (ii) the time after the training phase also requires high processing power, (iii) they are very computationally expensive, requiring large amounts of memory and computational resources, and it is not easy to transfer them to other problems, (iv) they are hard to describe and are not completely understood (black box), and (v) most of the literature is based on the MIT-BIH or PTB databases, which do not cover most of the crucial arrhythmias. This paper proposes a new deep learning approach based on machine learning for detecting MI and CDs using large PTB-XL ECG data. First, all challenging issues of these heart signals have been considered, as the signal data are from different datasets and the data are filtered. After that, the MI and CD signals are fed to the deep learning model to extract the deep features. In addition, a new custom activation function is proposed, which has fast convergence to the regular activation functions. Later, these features are fed to an external classifier, such as a support vector machine (SVM), for detection. The efficiency of the proposed method is demonstrated by the experimental findings, which show that it improves satisfactorily with an overall accuracy of 99.20% when using a CNN for extracting the features with an SVM classifier. [ABSTRACT FROM AUTHOR]

Ultra-reliable low-latency communication (uRLLC) is a group of fifth-generation and sixth-generation (5G/6G) cellular applications with special requirements regarding latency, reliability, and availability. Most of the announced 5G/6G applications are uRLLC that require an end-to-end latency of milliseconds and ultra-high reliability of communicated data. Such systems face many challenges since traditional networks cannot meet such requirements. Thus, novel network structures and technologies have been introduced to enable such systems. Since uRLLC is a promising paradigm that covers many applications, this work considers reviewing the current state of the art of the uRLLC. This includes the main applications, specifications, and main requirements of ultra-reliable low-latency (uRLL) applications. The design challenges of uRLLC systems are discussed, and promising solutions are introduced. The virtual and augmented realities (VR/AR) are considered the main use case of uRLLC, and the current proposals for VR and AR are discussed. Moreover, unmanned aerial vehicles (UAVs) are introduced as enablers of uRLLC. The current research directions and the existing proposals are discussed. [ABSTRACT FROM AUTHOR]

With the emergence of various new Internet of Things (IoT) devices and the rapid increase in the number of users, enormous services and complex applications are growing rapidly. However, these services and applications are resource-intensive and data-hungry, requiring satisfactory quality-of-service (QoS) and network coverage density guarantees in sparsely populated areas, whereas the limited battery life and computing resources of IoT devices will inevitably become insufficient. Unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is one of the most promising solutions that ensures the stability and expansion of the network coverage area for these applications and provides them with computational capabilities. In this paper, computation offloading and resource allocation are jointly considered for multi-user multi-UAV-enabled mobile edge-cloud computing systems. First, we propose an efficient resource allocation and computation offloading model for a multi-user multi-UAV-enabled mobile edge-cloud computing system. Our proposed system is scalable and can support increases in network traffic without performance degradation. In addition, the network deploys multi-level mobile edge computing (MEC) technology to provide the computational capabilities at the edge of the radio access network (RAN). The core network is based on software-defined networking (SDN) technology to manage network traffic. Experimental results demonstrate that the proposed model can dramatically boost the system performance of the system in terms of time and energy. [ABSTRACT FROM AUTHOR]

Smart cities using the Internet of Things (IoT) can operate various IoT systems with better services that provide intelligent and efficient solutions for various aspects of urban life. With the rapidly growing number of IoT systems, the many smart city services, and their various quality of service (QoS) constraints, servers face the challenge of allocating limited resources across all Internet-based applications to achieve an efficient per-formance. The presence of a cloud in the IoT system of a smart city results in high energy con-sumption and delays in the network. Edge computing is based on a cloud computing framework where computation, storage, and network resources are moved close to the data source. The IoT framework is identical to cloud computing. The critical issue in edge computing when executing tasks generated by IoT systems is the efficient use of energy while maintaining delay limitations. In this paper, we study a multicriteria optimization approach for resource allocation with distributed edge computing in IoT-based smart cities. We present a three-layer network architecture for IoT-based smart cities. An edge resource allocation scheme based on an auctionable approach is proposed to ensure efficient resource computation for delay-sensitive tasks. [ABSTRACT FROM AUTHOR]

In recent years, intelligent sensing has gained significant attention because of its autonomous decision-making ability to solve complex problems. Today, smart sensors complement and enhance the capabilities of human beings and have been widely embraced in numerous application areas. Artificial intelligence (AI) has made astounding growth in domains of natural language processing, machine learning (ML), and computer vision. The methods based on AI enable a computer to learn and monitor activities by sensing the source of information in a real-time environment. The combination of these two technologies provides a promising solution in intelligent sensing. This survey provides a comprehensive summary of recent research on AI-based algorithms for intelligent sensing. This work also presents a comparative analysis of algorithms, models, influential parameters, available datasets, applications and projects in the area of intelligent sensing. Furthermore, we present a taxonomy of AI models along with the cutting edge approaches. Finally, we highlight challenges and open issues, followed by the future research directions pertaining to this exciting and fast-moving field. [ABSTRACT FROM AUTHOR]

With the rapid growth of connected devices, new issues emerge, which will be addressed by boosting capacity, improving energy efficiency, spectrum usage, and cost, besides offering improved scalability to handle the growing number of linked devices. This can be achieved by introducing new technologies to the traditional Internet of Things (IoT) networks. Visible light communication (VLC) is a promising technology that enables bidirectional transmission over the visible light spectrum achieving many benefits, including ultra-high data rate, ultra-low latency, high spectral efficiency, and ultra-high reliability. Light Fidelity (LiFi) is a form of VLC that represents an efficient solution for many IoT applications and use cases, including indoor and outdoor applications. Distributed edge computing is another technology that can assist communications in IoT networks and enable the dense deployment of IoT devices. To this end, this work considers designing a general framework for IoT networks using LiFi and a distributed edge computing scheme. It aims to enable dense deployment, increase reliability and availability, and reduce the communication latency of IoT networks. To meet the demands, the proposed architecture makes use of MEC and fog computing. For dense deployment situations, a proof-of-concept of the created model is presented. The LiFi-integrated fog-MEC model is tested in a variety of conditions, and the findings show that the model is efficient. [ABSTRACT FROM AUTHOR]

Although the Software-Defined Network (SDN) is a well-controlled and efficient network but the complexity of open flow switches in SDN causes multiple issues. Many solutions have been proposed so far for the prevention of errors and mistakes in it but yet, there is still no smooth transmission of pockets from source to destination specifically when irregular movements follow the destination host in SDN, the errors include packet loss, data compromise etc. The accuracy of packets received at their desired destination is possible if networks for pockets and hosts are monitored instead of analysis of network snapshot statistically for the state, as these approaches with open flow switches, discover bugs after their occurrence. This article proposes a design to achieve the said objective by defining the Intelligent Transmission Control Layer (ITCL) layer. It monitors all the connections of end hosts at their specific locations and performs necessary settlements when the connection state changes for one or multiple hosts. The layer informs the controller regarding any state change at one period and controller collects information of end nodes reported via ITCL. Then, updates flow tables accordingly to accommodate a location-change scenario with a route-change policy. ICTL is organized on prototype-based implementation using the popular POX platform. In this paper, it has been discovered that ITCL produces efficient performance in the trafficking of packets and controlling different states of SDN for errors and packet loss. [ABSTRACT FROM AUTHOR]

Security is one of the most important aspects of data transmission. To provide controlled access to the network, user authorization and authentication are often used with the help of an AAA server. RADIUS servers provide users with access to data, user authentication, and configuration information. When designing networks with such access control method implementation, it is necessary to understand how the characteristics of the communication channel affect the switching time of IP-TV channels, and therefore the overall quality of IPTV services. The principles of the main protocols for IP-TV using a RADIUS server are described. The main parameters of the communication channel were identified. The mathematical model and the graphs demonstrate how IP-TV service access time depends on telecommunication channel parameters. The results of a practical experiment are presented to prove the formed mathematical model. The results of a practical experiment and theoretical calculation are compared. [ABSTRACT FROM AUTHOR]

With the recent advances in the fifth-generation cellular system (5G), enabling vehicular communications has become a demand. The vehicular ad hoc network (VANET) is a promising paradigm that enables the communication and interaction between vehicles and other surrounding devices, e.g., vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) communications. However, enabling such networks faces many challenges due to the mobility of vehicles. One of these challenges is the design of handover schemes that manage the communications at the intersection of coverage regions. To this end, this work considers developing a novel seamless and efficient handover scheme for V2X-based networks. The developed scheme manages the handover process while vehicles move between two neighboring roadside units (RSU). The developed mechanism is introduced for multilane bidirectional roads. The developed scheme is implemented by multiple-access edge computing (MEC) units connected to the RSUs to improve the implementation time and make the handover process faster. The considered MEC platform deploys an MEC controller that implements a control scheme of the software-defined networking (SDN) controller that manages the network. The SDN paradigm is introduced to make the handover process seamless; however, implementing such a controlling scheme by the introduction of an MEC controller achieves the process faster than going through the core network. The developed handover scheme was evaluated over the reliable platform of NS-3, and the results validated the developed scheme. The results obtained are presented and discussed. [ABSTRACT FROM AUTHOR]

The spreading of accidental or malicious misinformation on social media, specifically in critical situations, such as real-world emergencies, can have negative consequences for society. This facilitates the spread of rumors on social media. On social media, users share and exchange the latest information with many readers, including a large volume of new information every second. However, updated news sharing on social media is not always true.In this study, we focus on the challenges of numerous breaking-news rumors propagating on social media networks rather than long-lasting rumors. We propose new social-based and content-based features to detect rumors on social media networks. Furthermore, our findings show that our proposed features are more helpful in classifying rumors compared with state-of-the-art baseline features. Moreover, we apply bidirectional LSTM-RNN on text for rumor prediction. This model is simple but effective for rumor detection. The majority of early rumor detection research focuses on long-running rumors and assumes that rumors are always false. In contrast, our experiments on rumor detection are conducted on real-world scenario data set. The results of the experiments demonstrate that our proposed features and different machine learning models perform best when compared to the state-of-the-art baseline features and classifier in terms of precision, recall, and F1 measures. [ABSTRACT FROM AUTHOR]