Papers by Mohamed El Ghmary
Lecture notes in networks and systems, 2023
Lecture notes in networks and systems, 2023
Journal of Engineering Science and Technology Review
International Journal of Power Electronics and Drive Systems (IJPEDS)
This paper focuses on the control of permanent magnet synchronous motors (PMSMs) utilizing a non-... more This paper focuses on the control of permanent magnet synchronous motors (PMSMs) utilizing a non-linear adaptive PI-Backstepping design and a model of harmonics reduction approach that uses an active shunt filter followed by a cascade bandpass filter. While conventional backstepping may assure the system's stability, it is often imprecise. It creates a significant amount of static error, which has a detrimental effect on the system's behavior, such as disruptions and loads that might arise in industrial settings. We can assure minimum fixed errors and considerable interaction with uncertainty by integrating the PI controller with adaptive backstepping through robust Lyapunov functions. Numerical simulations are used to demonstrate the proposed controller's effectiveness.
International Journal of Advanced Computer Science and Applications
Mobile Edge Computing (MEC) is a new method to overcome the resource limitations of mobile device... more Mobile Edge Computing (MEC) is a new method to overcome the resource limitations of mobile devices by enabling Computation Offloading (CO) with low latency. This paper proposes a multiuser multi-task effective system to offload computations for MEC that guarantees in terms of energy, latency for MEC. To begin, radio and computation resources are integrated to ensure the efficient utilization of shared resources when there are multiple users. The energy consumed is positively correlated with the power of transmission and the local CPU frequency. The values can be adjusted to accommodate multi-tasking in order to minimize the amount of energy consumed. The current methods for offloading aren't appropriate when multiple tasks and multiple users have high computing density. Additionally, this paper proposes a multiuser system that includes multiple tasks and high-density computing that is efficient. Simulations have confirmed the MultiUser Multi-Task Offloading Algorithm (MUMTOD). The results in terms of execution time and energy consumption are extremely positive. This improves the effectiveness of offloading as well as reducing energy consumption.
International Journal of Interactive Mobile Technologies (iJIM)
Facial expressions constitute one of the most effective and instinctive methods that allow people... more Facial expressions constitute one of the most effective and instinctive methods that allow people to communicate their emotions and intentions. In this context, the both Machine Learning (ML) and Convolutional Neural Networks (CNNs) have been used for emotion recognition. Efficient recognition systems are required for good human-computer interaction. However, facial expression recognition is related to several methods that impact the performance of facial recognition systems. In this paper, we demonstrate a state-of-the-art of 65% accuracy on the FER2013 dataset by leveraging numerous techniques from recent research and we also proposed some new methods for improving accuracy by combining CNN architectures such as VGG-16 and Resnet-50 with auxiliary datasets such as JAFFE and CK. To predict emotions, we used a second approach based on geometric features and facial landmarks to calculate and transmit the feature vector to the SVM model. The results show that the ResNet50 model outp...
International Journal of Interactive Mobile Technologies (iJIM)
The appearance of Edge Computing with the possibility to bring powerful computation servers near ... more The appearance of Edge Computing with the possibility to bring powerful computation servers near the mobile device is a major stepping stone towards better user experience and resource consumption optimization. Due to the Internet of Things invasion that led to the constant demand for communication and computation resources, many issues were imposed in order to deliver a seamless service within an optimized cost of time and energy, since most of the applications nowadays require real response time and rely on a limited battery resource. Therefore, Mobile Edge Computing is the new reliable paradigm in terms of communication and computation consumption by the mobile devices. Mobile Edge Computing rely on computation offloading to surpass cloud-based technologies issues and break the limitations of mobile devices such as computing, storage and battery resources. However, computation offloading is not always the optimal choice to adopt, which makes the offloading decision a crucial part...
2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech)
In Mobile Cloud Computing, Smart Mobile Devices (SMDs) and Cloud Computing are combined to create... more In Mobile Cloud Computing, Smart Mobile Devices (SMDs) and Cloud Computing are combined to create a new infrastructure that allows data processing and storage outside the device. The Internet of Things refers to the billions of physical devices that are connected to the Internet. With the rapid development of these, it is clear that the requirements are largely based on the need for autonomous devices to facilitate the services required by applications that require rapid response time and flexible mobility. In this article, we study the management of computational resources and the trade-off between the consumed energy by an SMD and the processing time of its tasks. For this, we define a system model, a problem formulation and offer heuristic solutions for offloading tasks in order to jointly optimize the allocation of computing resources under limited energy and sensitive latency. In addition, we use the residual energy of the SMD battery and the sensitive latency of its tasks in defining the weighting factor of energy consumption and processing time.
2019 International Conference on Wireless Networks and Mobile Communications (WINCOM), 2019
Mobile Edge Computing (MEC) is a promising new technology that offers new opportunities for energ... more Mobile Edge Computing (MEC) is a promising new technology that offers new opportunities for energy consumption optimization, privacy preservation, and network traffic bottlenecks” reduction. Besides, MEC-based computation tasks offloading can achieve lower latencies and energy consumption. However, with the multi-task multi-user setting, the offloading decisions become hard and critical. Indeed, the communication and processing resources as well as the resulting processing delays and the consumed energies have to be carefully considered. In this paper, we consider a multi-policy offloading scenario where each mobile device holds a list of heavy tasks. Each task is further characterized by its proper processing deadline. Therefore, we designed the corresponding optimization problem that minimizes a weighted-sum function that jointly considers energy consumption, processing delays, and the unsatisfied tasks' workloads. Due to the short decision time constraint in the studied system and the NP-hardness of the obtained problem, we decomposed it using two sub-problems. Then, we proposed a solution to each sub-problem. With the aim of evaluating these solutions., we performed a set of simulation experiments to compare their performance with relevant state of the art method. Finally., the obtained execution times are very satisfactory for moderate number of tasks.
IAES International Journal of Artificial Intelligence (IJ-AI), 2021
The whole world is inundated with smaller devices equipped with wireless communication interfaces... more The whole world is inundated with smaller devices equipped with wireless communication interfaces. At the same time, the amount of data generated by these devices is becoming more important. The smaller size of these devices has the disadvantage of being short of processing and storage resources (memory, processes, energy,...), especially when it needs to process larger amounts of data. In order to overcome this weakness and process massive data, devices must help each other. A low-resource node can delegate the execution of a set of computionly heavy tasks to another machine in the network to process them for it. The machine with sufficient computational resources must also deposit the appropriate environment represented by the adapted virtual machine. Thus, in this paper, in order to migrate the virtual machine to an edge server in a mobile edge computing environment, we have proposed an approach based on artificial intelligence. More specifically, the main idea of this paper is t...
Lecture Notes in Mechanical Engineering, 2020
The Mobile Edge Computing (MEC) provides leading-edge services to multiple smart mobile devices (... more The Mobile Edge Computing (MEC) provides leading-edge services to multiple smart mobile devices (SMDs). Besides, computation offloading is a promising service in the 5G networks: it reduces battery drain and applications' execution time. These SMDs generally possess limited battery power and processing capacity. In addition, the local CPU frequency allocated to processing has a huge impact on SMDs energy consumption. In this paper, we consider a multiuser MEC system, where multiple SMDs demand computation offloading to a MEC server. The weighted sum of the overall energy consumptions and latencies represent the optimization problem's objective. In this problem, we jointly optimize offloading decisions, radio resource allocation and local computational resources allocation. The results obtained using our heuristic scheme show that it achieves good performance in terms of energy and latency. Accordingly, its achievement is encouraging compared to both cases where we perform local execution only or complete tasks offloading only.
Embedded Systems and Artificial Intelligence, 2020
The deployment of edge computing forms a two-tier mobile computing network where each computation... more The deployment of edge computing forms a two-tier mobile computing network where each computation task can be processed locally or at the edge node. In this paper, we consider a single mobile device equipped with a list of heavy off-loadable tasks. Our goal is to jointly optimize the offloading decision and the computing resource allocation to minimize the overall tasks processing time. The formulated optimization problem considers both the dedicated energy capacity and the processing deadlines. Therefore, as the obtained problem is NP-hard and we proposed a simulated annealing-based heuristic solution scheme. In order to evaluate and compare our solution, we carried a set of simulation experiments. Finally, the obtained results in terms of total processing time are very encouraging. In addition, the proposed scheme generates the solution within acceptable and feasible timeframes.
2018 9th International Symposium on Signal, Image, Video and Communications (ISIVC), 2018
Background: Epithelioid angiomyolipoma (EAML)-a recently recognized pathologic entity-reportedly ... more Background: Epithelioid angiomyolipoma (EAML)-a recently recognized pathologic entity-reportedly can develop at various anatomical sites, but rarely in the gynecological region, particularly in the uterus. Case presentation: We present a rare case of extrarenal EAML that developed within the uterus of a 57year old woman without tuberous sclerosis. Magnetic resonance imaging (MRI) showed that the tumor was mainly composed of mature adipose tissue. Conclusion: This case offers new insight into the appearance of extrarenal EAML in the uterus on MRI.
Proceedings of the 2nd International Conference on Advanced Technologies for Humanity, 2020
Mobile Edge Computing (MEC) extends the Cloud Computing paradigm to the edge of the network, thus... more Mobile Edge Computing (MEC) extends the Cloud Computing paradigm to the edge of the network, thus enabling a new breed of applications and services; hence it is considered as a promising technique to meet the demands of computing intensive and delay sensitive applications. This article describes the value for a trade off between processing time and energy in a MEC environment. The optimization problem formulated takes into account both the processing time and the dedicated energy capacity. To solve this problem we proposed a heuristic solution scheme. To evaluate our solution, we performed a comparative study with a brute force search solution. The results obtained are entirely very satisfactory.
This article describes a processing time, energy and computing resources optimization in a Mobile... more This article describes a processing time, energy and computing resources optimization in a Mobile Edge Computing (MEC). We consider a mobile user MEC system, where a smart mobile device (SMD) demand computation offloading to a MEC server. For that, we consider a SMD contains a set of heavy tasks that can be offloadable. The formulated optimization problem takes into account both the dedicated energy capacity and the processing times. We proposed a heuristic solution schema. To evaluate our solution, we realized a range of simulation experiments. The results obtained in terms of treatment time and energy consumption are very.
2019 5th International Conference on Optimization and Applications (ICOA), 2019
Mobile edge computing (MEC) provides remote computation capacity at the edge of mobile networks i... more Mobile edge computing (MEC) provides remote computation capacity at the edge of mobile networks in close proximity to smart mobile devices (SMDs). These devices generally possess limited processing capacity and battery power. Hence, heavy tasks that require a lot of computation and are energy consuming must be offloaded to a MEC server. Actually, it must consider the wireless network state, the number of SMDs requesting the computation offloading, and the available radio resources. In this paper, we consider a multi-mobile users MEC system, where multiple SMDs demand computation offloading to a MEC server, and we take into account the presence of particular subscribers having priority for computation offloading services. The purpose of this paper is to jointly optimize task offloading decisions and the allocation of critical radio resources while maintaining the priority of certain mobile device users and minimizing overall power consumption. Therefore, we have formulated a bi-objective optimization problem which is NP-Hard. Accordingly, with the use of the weighted aggregation approach, we propose and evaluate a heuristic that, in addition to minimizing the overall energy, minimizes the number of penalized SMDs in case where the radio resources are critical. The obtained results in terms of energy and satisfaction of the SMDs are very encouraging.
2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), 2019
Mobile edge computing (MEC) provides remote computation capacity at the edge of mobile networks i... more Mobile edge computing (MEC) provides remote computation capacity at the edge of mobile networks in close proximity to smart mobile devices (SMDs). These devices generally possess limited processing capacity and battery power. Hence, heavy tasks that require a lot of computation and are energy consuming must be offloaded to a MEC server. This choice remains the only option in some circumstance, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Moreover, the offloading process must consider the wireless network state, the number of SMDs requesting computation offloading, the available radio resources, and particularly the local available battery power. In this paper, we consider a multi-user MEC system where multiple SMDs demand computation offloading. In order to minimize the overall energy consumption while maintaining the batteries lifetime, we formulate an optimization problem. In this problem, we jointly optimize offloading decisions, radio resource allocation and local computational resources allocation. We propose and evaluate a heuristic scheme named Overall Energy Minimization by Resources Partitioning (OEMRP). The obtained results in terms of energy consumption are very encouraging.
Multimedia Tools and Applications, 2021
The Mobile Edge Computing (MEC) environment provides leading-edge services to smart mobile device... more The Mobile Edge Computing (MEC) environment provides leading-edge services to smart mobile devices (SMDs). Besides, computation offloading is a promising service in 5G: it reduces battery drain and applications’ execution time. In this context, we consider a general system consisting of a multi-cell communication network where each base station (BS) is equipped with a MEC server to provide computation offloading services to nearby mobile users. In addition, each SMD handles multiple independent offloadable heavy tasks that are latency-sensitive. The purpose of this article is to jointly optimize tasks’ offloading decisions as well as the allocation of critical radio resources while minimizing the overall energy consumption. Therefore, we have formulated a bi-objective optimization problem that is NP-hard. Because of the short decision time constraint, the optimal solution implementation is infeasible. Accordingly, with the use of the weighted aggregation approach, we propose Intelligent Truncation based Hybrid Local Search (ITHLS) solution. In critical radio resources situations, our solution jointly minimizes the number of penalized SMDs and the overall consumed energy. Finally, simulation experiments were realized to study the ITHLS solution performance compared to some effective state of the art solutions, and the simulation results in terms of decision-making time, energy and number of truncated SMDs are very promising.
Pervasive and Mobile Computing, 2021
Abstract Computation offloading within Mobile Edge Computing (MEC) networks is a promising new te... more Abstract Computation offloading within Mobile Edge Computing (MEC) networks is a promising new technique, especially in the 5G era. This technique offers leading-edge services to the users of Smart Mobile Devices (SMDs) to reduce the processing time and battery drain. Thus, SMDs tend to offload their heavy processing tasks to preserve battery power and benefit from an important processing power. However, in the era of the Internet of Things (IoT), several subscribers will compete for the available provided resources. Thus, we consider subscribers with a priority property fixed by their contracts with the service provider. In this work, we study a multi-server MEC network with multiple base stations where each one is equipped with a MEC server and provides offloading services to nearby users. Accordingly, we consider the energy consumption, the critical situations of radio resources’ insufficiency as well as a penalty function based on the SMDs’ priority. Therefore, we formulated a bi-objective optimization problem that jointly minimizes the overall energy consumption and the penalty function while allocating the local processing frequencies for the SMDs, their transmit powers and the radio resources allocated by the Base Station (BS). Consequently, based on the weighted aggregation approach, we propose and study a heuristic solution called Resources Allocation with Priority Devices (RAPD). Finally, simulation experiments were realized to study the RAPD solution performance compared to some effective state of the art solutions, and the simulation results in terms of decision-making time, energy and penalty are very promising.
International Journal of Electrical and Computer Engineering (IJECE), 2020
In recent years, the importance of the mobile edge computing (MEC) paradigm along with the 5G, th... more In recent years, the importance of the mobile edge computing (MEC) paradigm along with the 5G, the Internet of Things (IoT) and virtualization of network functions is well noticed. Besides, the implementation of computation-intensive applications at the mobile device level is limited by battery capacity, processing capabalities and execution time. To increase the batteries life and improve the quality of experience for computationally intensive and latency-sensitive applications, offloading some parts of these applications to the MEC is proposed. This paper presents a solution for a hard decision problem that jointly optimizes the processing time and computing resources in a mobile edge-computing node. Hence, we consider a mobile device with an offloadable list of heavy tasks and we jointly optimize the offloading decisions and the allocation of IT resources to reduce the latency of tasks’ processing. Thus, we developped a heuristic solution based on the simulated annealing algorith...
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Papers by Mohamed El Ghmary