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Islanded microgrids, powered by renewable energy sources, offer a sustainable electricity solution for remote areas. However, maintaining frequency stability in these systems remains a challenge due to the intermittent nature of renewables. This research proposes an approach to enhance microgrid stability by integrating a green hydrogen energy storage system (GHESS) and employing advanced AI-based control strategies. The GHESS plays a pivotal role in storing excess renewable energy as hydrogen and then converting it back to electricity when needed, reducing reliance on traditional backup generators. To optimize microgrid performance, a hybrid single-neuron PID (SNPID) controller, augmented by machine learning techniques, is developed and compared against conventional proportional, integral, and derivative PID and fuzzy self-tuning PID (FSTPID) controllers. The system’s performance was evaluated using four realistic scenarios. In all cases, the SNPID controller significantly outperformed the existing options. It achieved a 58% reduction in frequency fluctuations compared to the fuzzy self-tuning PID (FSTPID) controller and an impressive 87% reduction compared to the traditional PID controller. The Simulation results underscore the SNPID controller’s exceptional performance in frequency stability, emphasizing the transformative potential of AI for microgrid management.

Recently, the huge growth in multimedia demand derived from smart devices and Internet of Things (IoT) development requires efficient heterogeneous networks with high data rates. To fulfill these requirements, the exploitation of Unmanned Aerial Vehicles (UAVs) enabled with multiple radio access for providing efficient connectivity should be considered, since it offers flexibility, ease of deployment, and on-demand service for wireless devices. In this paper, the idea of deploying Multiple Radio Access Technologies (Multi-RAT) base stations on a UAV is proposed, in order to utilize the unlicensed spectrum. The problem of optimizing the Multi-RAT UAV’s location along with the wireless devices’ association to maximize the total system throughput considering a heterogeneous LTE and WLAN ground network is investigated. To solve this problem, we propose a novel framework based on reinforcement learning and regret matching algorithms, such that the Q-learning algorithm is used to find the Multi-RAT UAV’s optimum location, while regret matching is used to solve the optimum users’ association. Moreover, a K-means clustering algorithm is adopted as an initialization phase to speed up the convergence of the proposed solution. Simulation results show the significance of the proposed idea of deploying Multi-RAT base stations on a UAV. Further, the performance analysis shows the effectiveness of the proposed framework by applying K-means as an initialization phase.

Ambient backscatter communication (ABC) is considered as a promising paradigm for meeting the 6G massive Internet of Things (IoT) requirements which is expected to revolutionize our world. In this paper, a new multimode matching game and machine learning-based IoT ambient backscatter communication scheme is proposed to maximize the ABC system rate and capacity over the LTE and Wi-Fi multi-RAT heterogeneous network, thereby supporting the 6G green massive IoT communication. The proposed algorithm is designed to support different rate and capacity requirements for different massive Machine Type Communication (mMTC) use cases such as sensor networks, smart grid, agriculture and low data rate Ultra Reliable Low Latency Communication (URLLC) use cases such as tactile interaction. The proposed optimization algorithm runs into two phases, the first one is a matching game-based algorithm that selects the optimum association between the IoT tags and the primary users (PU) downlink signals from a specific base station which maximizes the IoT tags rate while minimizing the resulting interference to the PU. Each IoT tag can ride the PU downlink signal using one of three different riding modes according to the required IoT ABC system rate and capacity, whereas mode 1 allows multiple IoT tags to ride the whole PU downlink signal resource blocks, in mode 2 each IoT tag can ride only one subcarrier from the PU downlink signal resource blocks, while in mode 3 multiple IoT tags can ride the same subcarrier from the PU downlink signal resource blocks. In addition, unmanned aerial vehicles (UAVs) flying HetNodes equipped with LTE and Wi-Fi receivers are used as backscatter receivers to receive the IoT tags uplink backscattered signals, so the second optimization phase is formulated to maximize the total sum rate of the ABC system by dividing its service area into clusters using the enhanced unsupervised k-means algorithm, also the enhanced k-means algorithm finds the optimum location of each cluster’s serving UAV flying HetNode that maximizes the channels gain between the IoT tags and the serving UAV flying HetNode in order to maximize the total system rate. The system model was implemented within the MATLAB environment where simulations across the various scenarios are conducted to assess the effectiveness of the proposed algorithm. Simulation results and the performance analysis demonstrated that the proposed algorithm can support the required rate for the most mMTC and low data rate URLLC IoT applications with average IoT tag rates in the range of 15 Kbps to 115 Kbps, and outperforms the algorithm-free riding technique in the case of massive IoT applications. The proposed mode 2 (first enhanced mode) achieves the best performance in terms of the average IoT tags rate and the total system rate with the lowest interference to the primary system users, on the other hand, mode 3 (second enhanced mode) improves the system capacity with maximum IoT tags satisfaction ratio. The capacity and satisfaction ratio of the proposed mode 3 outperforms mode 1 by 300 % and 138% respectively and outperforms mode 2 by 2,000 % and 420 % respectively. The proposed algorithm reduces the interference power to the PUs on the average by $1:\left(15.69 \times 10^{-12}\right)$ relative to the algorithm-free riding technique. From the result, we can conclude that the proposed algorithm supports different IoT applications and achieves the required data rates with minimal effect on the primary system keeping the PU’s data rate within the required range compared to the algorithm-free riding technique with the cost of higher time complexity.

The emergence of the sixth generation (6G) era has highlighted the importance of Network Slicing (NS) technology as a promising solution for catering the diverse service requests of users. With the presence of a large number of devices with different service requests and since each service has different goals and requirements; efficiently allocating Resource Blocks (RBs) to each network slice is a challenging task to meet the desired Quality of Service (QoS) standards. However, it is worth noting that the majority of research efforts have primarily concentrated on cellular technologies, leaving behind the potential benefits of utilizing unlicensed bands to alleviate traffic congestion and enhance the capacity of existing LTE networks. In this paper we propose a novel idea by exploiting LTE-WLAN Aggregation technology (LWA) in Multi-Radio Access Technology (RAT) Heterogeneous Networks (HetNet), aiming to solve radio resource allocation problem based on the Radio Access Network (RAN) slicing and 5G New Radio (NR) scalable numerology technique. A joint optimization problem is proposed by jointly finding an efficient resource allocation ratio for each slice in each Base Station (BS) and by finding the optimum value of scalable numerology with the objective of maximizing users’ satisfaction. In order to solve this problem, a novel three-stage framework is proposed which is based on channel state information as a pre-association stage, Reinforcement Learning (RL) algorithm as finding the optimum value of slice resource ratio and scalable numerology, and finally Regret Learning Algorithm (RLA) as users’ re-association phase. Furthermore, a comprehensive performance evaluation is conducted against different baseline approaches. The simulation results show that our proposed model balances and achieves improvement in users’ satisfaction by deploying the proposed Multi-RAT Het-Net architecture that leverages LWA technology.

Abstract In this paper, the flexibility of unmanned aerial vehicles (UAVs), as well as the benefits of coordinated multi-point (CoMP) transmission, are utilized for mitigating the interference in cellular networks. Specifically, the joint problem of CoMP clusters and UAVs’ trajectories is addressed for downlink transmission in a UAV-assisted cellular system. The problem is presented as a non-convex optimization problem that aims to maximize the sum rate of the ground users by taking into account the clustering, UAV mobility and backhaul capacity constraints. Since the formulated problem is known to be NP-hard, we partition it into two sub-problems. Particularly, by using coalitional game theory, the CoMP clusters are obtained with a given UAVs’ trajectories. Then, UAVs’ trajectories are optimized with given CoMP clusters using successive convex approximation technique. Based on the block coordinate descent method, the two sub-problems are solved alternatively until convergence. Numerical results are conducted and demonstrated the effectiveness of the proposed algorithm.

The efficient Virtual Reality (VR) data delivery in 6G cellular networks is challenging, as it requires high throughput and ultra-reliable low-latency communication. In this work, VR based Software-Defined Network (SDN) model is proposed for efficient uplink-downlink resource management in 6G networks that guarantees low latency and high data rates. The suggested solution achieves End-to-End (E2E) management. among VR users with centralized resource management involving supervised deep learning (SDL) and unsupervised deep learning (UDL). Comparative results is demonstrated, which show the performance differences between these approaches in 6G networks that improve the transmission of VR video, enable such improvement.

Recently, increasing demand for remote laboratories is noticed, due to the shifting of the educational systems from face-to-face education to online education through the internet. A Remote-controlled laboratory is based on the use of real instrumentations and components, in which experiments are completely conducted and controlled remotely by the students through the internet. Thus, it provides a hands-on learning experience for students on an online basis. In order to realize this increasing demand for remote laboratories, efficient management of the laboratory resources should be considered. In this work, a novel laboratory learning system is proposed, which provides the flexibility of deployment on any physical infrastructure, integrability with other different physical laboratory resources, and scalability. Moreover, a mathematical model for the laboratory learning system is formulated, to provide the basis for defining the optimization problem. The problem of minimizing the number of blocked time slots per acquiring student, by optimizing the association between the reserving students and the available resources at different time slots is also formulated, which reflects the maximization of resources utilization. Then, a matching game-based framework is proposed to optimally solve the formulated problem. Simulation results show the significance of the proposed matching game-based framework compared to other students to resources association schemes, in minimizing the number of blocked time slots per acquiring student and maximizing the resources utilization efficiency.

Due to the COVID-19 pandemic and the development of educational technology, e-learning has become essential in the educational process. However, the adoption of e-learning in sectors such as engineering, science, and technology faces a particular challenge as it needs a special Laboratory Learning Management System (LLMS) capable of supporting online lab activities through virtual and controlled remote labs. One of the most challenging tasks in designing such LLMS is how to assess a student’s performance while an experiment is being conducted and how stuttering students can be automatically detected while experimenting and providing the appropriate assistance. For this, a generic technique based on Artificial Intelligence (AI) is proposed in this paper for assessing student performance while conducting online labs and implemented as a performance evaluation module in the LLMS. The performance evaluation module is designed to automatically detect the student performance during the experiment run time and triggers the LLMS virtual assistant service to provide struggling students with the appropriate help when they need it. Also, the proposed performance assessment technique is used during the lab exam sessions to support the automatic grading process conducted by the LLMS Auto-Grading Module. The proposed performance evaluation technique has been developed based on analyzing the student’s mouse dynamics to work generally with any type of simulation or control software used by virtual or remote controlled laboratories; without the need for special interfacing. The study has been applied to a novel dataset built by the course instructors and students simulating a circuit on TinkerCad. Using mouse dynamics fetching, the system extracts features and evaluates them to determine if the student has built the experiment steps in the right way or not. A comparison study has been developed between different Machine Learning (ML) models and a number of performance metrics are calculated. The study confirmed that Artificial Neural Network (ANN) and Support Vector Machine (SVM) are the best models to be used for automatically evaluating student performance while conducting the online labs with a precision reaching up to 91%.

In this paper, the antenna array appropriate topologies and configurations are investigated to reduce the impact of large gain variation between array elements that is produced due to the mutual coupling. Moreover, the most appropriate antenna array topology that can realize a maximum system throughput and achieve fairness between users in terms of their spectral efficiency is determined. Our analysis focuses on the comparison between various array geometries (uniform planar array (UPA), circular array (CA), conformal array (CfA), uniform planar circular array (UPCA), centric planar circular array (CPCA) and, finally, conical array (CoA)) with half-wavelength dipole elements. The simulation results show that UPA, CA, and CfA have total efficiency around 96% at 2.6GHz. Moreover, all the proposed geometries have achieved Envelope Correlation Coefficient (ECC) less than 0.5, and high Diversity Gain (DG) that makes it suitable for MIMO. The CfA geometry has been elected as the most suitable topology, which can provide minimum gain variation, maximum system throughput, and achieve fairness between users’ spectral efficiency. Finally, curl antenna is considered in the designed antenna array to study the effect of antenna gain pattern. Two array geometries have been designed using the curl antenna, which are the UPA and the CfA. The CfA curl has insertion loss -30dB between the edge elements and achieve fairness index between the users up to 0.98733. Moreover, the results show that curl antenna geometries can achieve higher system throughput compared to half-wavelength dipole based array geometries.

The COVID-19 pandemic has made the world focus on providing effective and fair online learning systems. As a consequence, this paper proposed a new intelligent, fair assessment of online examinations for virtual and remotely controlled laboratory experiments running through Laboratory Learning Systems. The main idea is to provide students with an environment similar to being physically present in a Laboratory while conducting practical experiments and exams and detecting cheating with high accuracy at a minimal cost. Therefore, an intelligent assessment module is designed to detect cheating students by analyzing their mouse dynamics using Artificial Intelligence. The mouse interaction behavior method was chosen because it does not require any additional resources, such as a camera and eye tribe tracker, to detect cheating. Various AI algorithms, such as KNN, SVC, Random Forest, Logistic Regression, XGBoost, and LightGBM have been used to classify student mouse behavior to detect cheating, and many metrics are used to evaluate their performance. Moreover, experiments have been conducted on students answering online laboratory experimentations while cheating and when answering the exams honestly. Experimental results indicate that the LightGBM AI algorithm achieves the best cheat detection results up to an accuracy of 90%, precision of 88%, and degree of separation of 95%.

Restrained by the long term evolution (LTE) limited network capacity, WiFi technology is considered as one of the promising solutions to leverage the traffic load and enhance the LTE capacity. Exploiting both the licensed and unlicensed spectrum was the motivated key to standardize the LTE-wireless local area network (WLAN) aggregation (LWA) technology by 3GPP in Release 13. In this paper, we consider the user association problem in LWA-based Multiple Radio Access Technologies (Multi-RAT) Heterogeneous Networks (HetNet) in which three transmission modes are available (LTE, WiFi, and aggregation mode) and the user needs to select not only the wireless node that will associate with it, but also the used transmission mode. For this, a new user association algorithm that considers the joint node and mode selection is proposed in this paper. This association process is formulated as an optimization problem with the aim to maximize total network throughput. To solve this problem, a one-to-many matching game-based association algorithm is designed, where each user is matched to the best transmission mode/node according to well-developed utility function that considers the achieved data rate of each user as well as the proportional fairness among users. Simulation results have shown that our proposed algorithm outperforms comparable association techniques such as WLAN first, LTE first, and LTE-W in terms of system throughput, outage probability and fairness between users.

The ubiquitous existence of COVID-19 has required the management of congested areas such as workplaces. As a result, the use of a variety of inspiring tools to deal with the spread of COVID-19 has been required, including internet of things, artificial intelligence (AI), machine learning (ML), and geofencing technologies. In this work, an efficient approach based on the use of ML and geofencing technology is proposed to monitor and control the density of persons in workplaces during working hours. In particular, the workplace environment is divided into a number of geofences in which each person is associated with a set of geofences that make up their own cluster using a dynamic user-centric clustering scheme. Different metrics are used to generate a unique geofence digital signature (GDS) such as Wi-Fi basic service set identifier, Wi-Fi received signal strength indication, and magnetic field data, which can be collected using the person’s smartphone. Then, these metrics are utilized by different ML techniques to generate the GDS for each indoor geofence and each building geofence as well as to detect whether the person is in their cluster. In addition, a Layered-Architecture Geofence Division method is considered to reduce the processing overhead at the person’s smartphone. Our experimental results demonstrate that the proposed approach can perform well in a real workplace environment. The results show that the system accuracy is about 98.25% in indoor geofences and 76% in building geofences.