Ahmed Mostafa Abdelhaleem Abdelkhalek

Scopus Publications

Deep reinforcement learning for resource allocation and scalable numerology in NR-U enabled multi-RAT HetNets
Noha A. Elmosilhy, Mahmoud M. Elmesalawy, Ahmed M. Abd El-Haleem, Ibrahim I. Ibrahim
Scientific Reports, 2026
Leveraging the new radio technology in the unlicensed band (NR-U) can alleviate traffic congestion, enhance network capacity, and help mitigate the diversity in users’ service requests. In this paper, a multiple slice multi-radio access technology (RAT) heterogeneous network (HetNet) is considered, integrating the new radio (NR) technology in the licensed and unlicensed bands. An optimization problem is proposed aiming to maximize users’ satisfaction, defined by maximizing the achievable data rate while maintaining the minimum latency slice requirement. To solve the proposed optimization problem, an iterative framework is introduced that utilizes deep reinforcement learning (DRL) algorithm jointly with the regret learning algorithm (RLA) that efficiently solves users’ association problem considering coexisting Wi-Fi users, allocates radio resources for each slice and determines the optimum scalable numerology value in each slice. The simulation results show that our proposed model improves users’ satisfaction, achieving up to 70% user satisfaction compared with other baseline approaches.
DRL-Driven Edge-Aware Utility Optimization for Multi-Slice 6G Networks
Khaled M. Naguib, Soumaya Cherkaoui, Mahmoud M. Elmessalawy, Ahmed M. Abd El-Haleem, Ibrahim I. Ibrahim
IEEE Networking Letters, 2026
Virtual Reality (VR) services delivered over 6G networks demand ultra-low latency and high bandwidth to ensure seamless user experiences. This paper presents an intelligent resource allocation and edge caching framework for 6G O-RAN networks, leveraging Deep Q-Network (DQN) learning for optimizing edge caching and dynamic resource provisioning across multiple network slices within an O-RAN-compliant architecture.By incorporating DRL agents into the network control plane, the proposed system enables proactive and adaptive content distribution as well as real-time computational resource allocation that meets the quality-of-service demands of eMBB, URLLC, and especially the emerging MBRLLC slices essential for VR. Simulation results demonstrate that the DQN-based framework consistently outperforms traditional methods in reducing latency and improving throughput, leading to more reliable and responsive support for immersive VR applications in 6G environments.
Deep learning optimization of STAR-RIS for enhanced data rate and energy efficiency in 6G wireless networks
Amal Megahed, Ahmed. M. Abd El-Haleem, Mahmoud M. Elmesalawy, Ibrahim I. Ibrahim
Scientific Reports, 2025
Unlike traditional reflection-only reconfigurable intelligent surfaces (RISs), simultaneously transmitting and reflecting RISs (STAR-RISs) introduce an innovative technology. They expand the coverage area from half-space to full-space. This advancement provides new degrees of freedom (DoF) for controlling and optimizing signal propagation. This research explores the performance of the STAR-RIS in 6G wireless networks, comparing nearly passive STAR-RIS (NP-STAR) and active STAR (ASTAR) against nearly passive RIS (NP-RIS) and active RIS (ARIS) benchmarks. Through extensive simulations and deep learning (DL)-based optimization, we assess achievable data rates and spectral energy efficiency (SEE) across various system configurations, training dataset sizes, and user locations. Results show that in high-interference environments, NP-STAR configurations can sometimes exceed ASTAR implementations due to their ability to mitigate interference amplification. In addition, the deterioration in spectral energy efficiency (SEE) of active implementations compared to nearly passive ones confirms their greater consumption of energy. The exploration of STAR-RIS technology in 6G networks reveals key research gaps in prior works. These include limited studies on performance under real-world conditions, insufficient understanding of energy-spectral efficiency trade-offs, and the need for reliable DL optimization across scenarios. Practical deployment issues like power management and interference control also lack adequate research. Addressing these gaps is crucial for advancing STAR-RIS technology in this paper. This study emphasizes the importance of managing transmit power levels at base stations to control interference, with active setups particularly excelling in optimal channel conditions. Furthermore, DL approaches can effectively approximate genie-aided performance bounds with adequate training data, especially in complex channel scenarios. These insights provide practical guidance for deploying STAR-RIS technology in demanding wireless networks.
Beyond diagonal-IRS assisted UAVs in terahertz network utilizing twin-delayed deep deterministic policy gradient approach
Shereen S. Omar, Ahmed M. Abd EL-Haleem, Ibrahim I. Ibrahim, Amany M. Saleh
Telecommunication Systems, 2025
Leveraging Active and Nearly Passive Reconfigurable Intelligent Surfaces Using Deep Learning Algorithm for 6G Wireless Networks
Amal Megahed, Mahmoud M. Elmesalawy, Ahmed. M. Abd El‐Haleem, Ibrahim I. Ibrahim
Transactions on Emerging Telecommunications Technologies, 2025
Active Reconfigurable Intelligent Surface (ARIS) shows promise in boosting the desired signal at the receiver user. However, the “fully‐connected” architecture of ARIS needs high power due to additional active components. This paper adopts sub‐connected ARIS to enhance achieved data rates with good energy efficiency at the Cell Edge Users (CEUs) and addresses the “multiplicative fading” effect caused when the signal propagates through a longer path (i.e., the serving Base Station (BS)‐ARIS‐CEU) than the straight route across the serving BS and the CEUs. Additionally, a Nearly Passive RIS (NP‐RIS) is proposed to mitigate interfering signals from other BSs by creating destructive interference at the CEUs. The reflection matrix of the NP‐RIS is extracted using Deep Learning (DL) techniques, with a select few NP‐RIS reflecting elements being active. This model improves achieved data rates by around 58% for M = 16 RIS elements compared with the baseline model with the same number of elements in NP‐RIS without ARIS implementation. Moreover, the proposed model enhances data rates by approximately 31.8% compared with a baseline using negative resistance Reflecting Elements (RE). However, the Spectral Energy Efficiency (SEE) using the second baseline will be improved over the “fully‐connected” ARIS leading to the sub‐connected ARIS solution to improve the SEE by nearly 25%.
Optimizing data transmission in 6G software defined networks using deep reinforcement learning for next generation of virtual environments
Khaled Mohamed Naguib, Ibrahim Ismail Ibrahim, Mahmoud Mohamed Elmessalawy, Ahmed Mostafa Abdelhaleem
Scientific Reports, 2024
Data transmission of Virtual Reality (VR) plays an important role in delivering a powerful VR experience. This increasing demand on both high bandwidth and low latency. 6G emerging technologies like Software Defined Network (SDN) and resource slicing are acting as promising technologies for addressing the transmission requirements of VR users. Efficient resource management becomes dominant to ensure a satisfactory user experience. The integration of Deep Reinforcement Learning (DRL) allows for dynamic network resource balancing, minimizing communication latency and maximizing data transmission rates wirelessly. Employing slicing techniques further aids in managing distributed resources across the network for different services as enhanced Mobile Broadband (eMBB) and Ultra-Reliable and Low Latency Communications (URLLC). The proposed VR-based SDN system model for 6G cellular networks facilitates centralized administration of resources, enhancing communication between VR users. This innovative solution seeks to contribute to the effective and streamlined resource management essential for VR video transmission in 6G cellular networks. The utilization of Deep Reinforcement Learning (DRL) approaches, is presented as an alternative solution, showcasing significant performance and feature distinctions through comparative results. Our results show that implementing strategies based on DRL leads to a considerable improvement in the resource management process as well as in the achievable data rate and a reduction in the necessary latency in dynamic and large scale networks.
Trajectory optimization of UAV-IRS assisted 6G THz network using deep reinforcement learning approach
Amany M. Saleh, Shereen S. Omar, Ahmed M. Abd El-Haleem, Ibrahim I. Ibrahim, Mostafa M. Abdelhakam
Scientific Reports, 2024
Terahertz (THz) wireless communication is a promising technology that will enable ultra-high data rates, and very low latency for future wireless communications. Intelligent Reconfigurable Surfaces (IRS) aiding Unmanned Aerial Vehicle (UAV) are two essential technologies that play a pivotal role in balancing the demands of Sixth-Generation (6G) wireless networks. In practical scenarios, mission completion time and energy consumption serve as crucial benchmarks for assessing the efficiency of UAV-IRS enabled THz communication. Achieving swift mission completion requires UAV-IRS to fly at maximum speed above the ground users it serves. However, this results in higher energy consumption. To address the challenge, this paper studies UAV-IRS trajectory planning problems in THz networks. The problem is formulated as an optimization problem aiming to minimize UAVs-IRS mission completion time by optimizing the UAV-IRS trajectory, considering the energy consumption constraint for UAVs-IRS. The proposed optimization algorithm, with low complexity, is well-suited for applications in THz communication networks. This problem is a non-convex, optimization problem that is NP-hard and presents challenges for conventional optimization techniques. To overcome this, we proposed a Deep Q-Network (DQN) reinforcement learning algorithm to enhance performance. Simulation results show that our proposed algorithm achieves performance compared to benchmark schemes.
Maximizing IoT Throughput with Optimized IRS-Assisted Symbiotic Radio
Gerges M. Salama, Samar Shaker Metwly, Emad G. Shehata, Ahmed M. Abd El-haleem
Ingenierie Des Systemes D Information, 2024
Symbiotic Radio (SR) is one of the techniques recognized by 6G for wireless communication networks performance enhancement.In this paper, SR is used to improve the performance of the Internet of Things (IoT) network by enabling IoT tags backscatter the neighbor smart phone primary signal rely on the None Orthogonal Multiple Access (NOMA) technique.Furthermore, Intelligent Reflecting Surfaces (IRS) are also proposed to enhance the channel Quality of Service (QoS); the service performance; between the IoT tags and the smartphones either using LTE or Wi-Fi network by smartly reconfiguring the signal propagation for performance improvement.We formulate an optimization problem to achieve the optimum location and phase shifts of the IRS, aiming to maximize the throughput of the IoT system.Proximal Policy Optimization (PPO) algorithm is introduced as a solution for this problem.The main idea of PPO is to minimize the divergence between the new and old policy while maximizing the expected reward.This is achieved by using a surrogate objective function that approximates the policy update.Simulation results demonstrate that the proposed algorithms can improve the total system data rate by an average of 40% above the system without using IRS and it also, improves the system capacity by 40% on average when compared to a system without the IRS scheme at smart phones =4 which serve tags =20.
Performance Evaluation of mMIMO NOMA as Applied to Aeronautical Communications System
Amir Hanna, Ahmed Abd El-Haleem, Ihab Ali
2024 International Telecommunications Conference Itc Egypt 2024, 2024
As the number of flights continues to rise daily, finding an optimal solution that can efficiently boost the spectral efficiency of aeronautical communication systems has become imperative. The proposed solution by ICAO is to use the L-band digital aeronautical communication system (LDACS) instead of the VHF band to handle future requirements. However, using the L-band for aeronautical communications is difficult because it affects legal systems. Many onboard systems that operate on L-band already exist, such as the joint tactical information distribution system (JTIDS), the military tactical air navigation (TACAN) system, and the distance measurement equipment (DME). The DME determines the slant distance between the aircraft and the ground station and is one of the most crucial systems. So, we need complex techniques to mitigate the interference with the LDACS system. Our proposed solution is using the NOMA access technique in the VHF digital system to enhance the capacity of the aeronautical communication system without utilizing any other bands to avoid interference, which requires complex techniques to mitigate. In our work, we investigate the maximization of spectral efficiency for NOMA-based massive MIMO for aeronautical communications by using different matching algorithms for aircraft pairing in NOMA, such as the Gale Shapley algorithm, then compare the same algorithm with different beamforming techniques and with different CTGS antennas number. Results demonstrate GS algorithm provides better spectral efficiency than fixed pairing NOMA. Results also show that the achievable spectral efficiency with the MR beamforming technique is lower than that for ZF beamforming. Thus, the optimal solution comprises GS pairing and ZF beamforming.
Geofencing-Based AR/VR System for Connecting and Monitoring Workplaces in the Metaverse Using Machine Learning (MetaWMS)
Noor El-Deen M. Mohamed, Ahmed M. Abd El-Haleem, Mahmoud M. Elmesalawy
Proceedings of the 12th International Japan Africa Conference on Electronics Communications and Computations Jac Ecc 2024, 2024
The COVID-19 pandemic has drastically transformed the global work environment, making remote work a fundamental necessity for numerous organizations. This shift highlighted the importance of seamless connectivity and effective monitoring to ensure productivity, collaboration, and security. Digital tools such as video conferencing, project management platforms, and instant messaging apps have become essential for creating engaging virtual workspaces. However, there remains a critical demand for innovative solutions to further enhance remote work environments. This research explores the integration of Augmented Reality (AR), Virtual Reality (VR), and the metaverse, alongside geofencing, Machine Learning (ML), and Deep Learning (DL), to create immersive and interactive virtual spaces. These technologies can significantly boost efficiency and productivity among remote employees. Geofencing is utilized to define virtual perimeters, enabling location-specific AR experiences. The proposed system comprises a mobile application, a web-based management platform, and a VR headset, designed to monitor and connect remote employees effectively. Experimental results demonstrate the system’s accuracy and efficacy in real workplace environments, showcasing its potential to transform remote work practices. The experimental results highlight the effectiveness of the proposed approach, achieving an average accuracy of 95% for indoor geofence detection in real-world AR loading scenarios.
Spectral-Efficient Aircraft Pairing for Massive MIMO NOMA in Aeronautical Communication
Amir K. Hanna, Ahmed M. Abd El-Haleem, Ihab A. Ali
Journal of Communications, 2024
Location and User Association Optimization in Multiple Radio Access UAV-Assisted Heterogeneous IoT Networks
Mohamed G. Anany, Mahmoud M. Elmesalawy, Ibrahim I. Ibrahim, Ahmed M. Abd El-Haleem
IEEE Access, 2024
Ambient Backscatter Communication System Empowered by Matching Game and Machine Learning for Enabling Massive IoT Over 6G HetNets
Amany A. Khalifa, Ahmed M. Abd El-Haleem, Mahmoud M. Elmesalawy, Hesham M. Z. Badr
IEEE Access, 2024
Joint Q-Learning Based Resource Allocation and Multi-Numerology B5G Network Slicing Exploiting LWA Technology
Noha A. Elmosilhy, Mahmoud M. Elmesalawy, Ibrahim I. Ibrahim, Ahmed M. Abd El-Haleem
IEEE Access, 2024
Deep learning-assisted reconfigurable intelligent surface for enhancing 6G mobile networks
Amal Megahed, Mahmoud M. Elmesalawy, Ibrahim I. Ibrahim, Ahmed M. Abd El‐Haleem
Transactions on Emerging Telecommunications Technologies, 2024
A Matching Game-Based Laboratory Learning System for Resources Management in Remote Laboratories
Ahmed M. Abd El-Haleem, Mohamed G. Anany, Mahmoud M. Elmesalawy, Eman Seraj El-Din Bakr
IEEE Access, 2023
Capacity Enhancement of Flying-IRS Assisted 6G THz Network Using Deep Reinforcement Learning
Shereen S. Omar, Ahmed M. Abd El-Haleem, Ibrahim I. Ibrahim, Amany M. Saleh
IEEE Access, 2023
DL-Based Minimizing Virtual Environment Resource Usage in 6G Cellular Networks
Khaled M. Naguib, Ibrahim I. Ibrahim, Mahmoud M. Elmesalawy, Ahmed M. Abd Elhaleem
IEEE Access, 2023
Deep Reinforcement Learning Algorithms for Location Optimization in Multi-RAT UAV-Assisted Heterogeneous Networks
M. G. Anany, Mahmoud M. Elmesalawy, Ibrahim I. Ibrahim, Ahmed M. Abd El-Haleem
5th Novel Intelligent and Leading Emerging Sciences Conference Niles 2023 Proceedings, 2023
Deep Reinforcement Learning Based Algorithm for Symbiotic Radio IoT Throughput Optimization in 6G Network
Gerges Mansour Salama, Samar Shaker Metwly, E. G. Shehata, Ahmed M. Abd El-Haleem
IEEE Access, 2023
Efficient Collaborative and Cooperative Laboratory Experimentation System for Online Engineering, Science and Technology Education
Ahmed M. Abd El-Haleem, Noha A. Elmosilhy, Abdullah I. Salama, Serag El Din Eman, Mahmoud M. Elmesalawy
Lecture Notes in Networks and Systems, 2023
Fairness Oriented H-NOMA Devices Association and Pairing in HCN Slicing Using Cooperation Games
Mai A. Riad, Osama M. El-Ghandour, Ahmed M. Abd El-Haleem
Ingenierie Des Systemes D Information, 2022
An Intelligent Approach for Fair Assessment of Online Laboratory Examinations in Laboratory Learning Systems Based on Student’s Mouse Interaction Behavior
Hadeer A. Hassan Hosny, Abdulrahman A. Ibrahim, Mahmoud M. Elmesalawy, Ahmed M. Abd El-Haleem
Applied Sciences Switzerland, 2022
Joint User-Slice Pairing and Association Framework Based on H-NOMA in RAN Slicing
Mai A. Riad, Osama El-Ghandour, Ahmed M. Abd El-Haleem
Sensors, 2022
A Machine Learning Approach for Movement Monitoring in Clustered Workplaces to Control COVID-19 Based on Geofencing and Fusion of Wi-Fi and Magnetic Field Metrics †
Ahmed M. Abd El-Haleem, Noor El-Deen M. Mohamed, Mostafa M. Abdelhakam, Mahmoud M. Elmesalawy
Sensors, 2022
Dynamic User-Centric Clustered Workplaces for COVID-19 Control Measures Based on Geofencing and Deep Learning
Ahmed Mostafa Abdelkhalek, Noor El-Deen Magdy Mohamed, Mostafa Mahmoud Abdelhakam, Mahmoud Mohamed Elmesalawy
ACM International Conference Proceeding Series, 2022
Determining Critical Success Factors for an Online Laboratory Learning System Using Delphi Method
Ahmed Mohamed Fahmy Yousef, Ahmed M. Abd El-Haleem, Mahmoud M. Elmesalawy
Ieir 2022 IEEE International Conference on Intelligent Education and Intelligent Research, 2022
AI Virtual Assistant for Online Laboratory Experiments Based on Multi-threshold Technique and Genetic Algorithm for Analyzing the Student's Mouse Interaction Activities
Mahmoud M. Elmesalawy, Ahmed M. Abd El-Haleem, Alaa Hamdy
Proceedings 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2022, 2022
Identifying Success Criteria for Sustainable AI-based Online Laboratory Courseware System
Ahmed Mohamed Fahmy Yousef, Ahmed M. Abd El-Haleem, Mahmoud M. Elmesalawy
IEEE Global Engineering Education Conference Educon, 2022
Generic Laboratory Authoring Tool for Virtual and Remote Controlled Laboratories
Hadeer A. Hassan Hosny, Noha A. Elmosilhy, Mahmoud M. Elmesalawy, Ahmed M. Abd El-Haleem
Icsec 2022 International Computer Science and Engineering Conference 2022, 2022
An LLMS Remotely Controlled Experiment for Smart Hydro Energy Storage and Irrigation System Powered by Photo-Voltaic Array and IoT
Helmy M. El Zoghby, Hadeer A. Hassan Hosny, Mahmoud M. Elmesalawy, Ahmed M. Abd El-Haleem
Proceedings 2022 International Conference on Artificial Intelligence of Things Icaiot 2022, 2022
A Generic AI-Based Technique for Assessing Student Performance in Conducting Online Virtual and Remote Controlled Laboratories
Ahmed M. Abd El-Haleem, Mohab Mohammed Eid, Mahmoud M. Elmesalawy, Hadeer A. Hassan Hosny
IEEE Access, 2022
A New Intelligent System for Evaluating and Assisting Students in Laboratory Learning Management System
Hadeer A. Hassan, Mohab Mohamed Eid, Mahmoud M. Elmesalawy, Ahmed M. Abd El-Haleem
Proceedings 2022 14th IEEE International Conference on Computational Intelligence and Communication Networks Cicn 2022, 2022
Geofencing-based Congestion Control in Workplaces Environment using Sequential Pattern Mining
Ahmed M. Abd El-Haleem, Mohamed G. Anany, Mostafa M. Abdelhakam, Noor El-Deen M. Mohamed, Mahmoud M. Elmesalawy
Niles 2022 4th Novel Intelligent and Leading Emerging Sciences Conference Proceedings, 2022
Violation Detection Technique for COVID-19 Self-Isolation and Control Measures using Wireless and Geofencing Technologies
Ahmed M. Abd El-Haleem, M. G. Anany, Abdullah I. Salama, Gamal A. Khalaf, Mahmoud M. Elmesalawy
2022 International Conference on Electronics Information and Communication Iceic 2022, 2022
IoT Enabled Geofencing-Based System for Monitoring and Enforcing COVID-19 Control Measures in Workplaces, Service Areas and Distributed Home Quarantine
Ahmed M. Abd El-Haleem, Abdullah I. Salama, M. G. Anany, Mostafa M. Abdelhakam, Gamal A. Khalaf, Mahmoud M. Elmesalawy
2022 9th International Conference on Electrical and Electronics Engineering Iceee 2022, 2022
AI-based Flexible Online Laboratory Learning System for Post-COVID-19 Era: Requirements and Design
Mahmoud M. Elmesalawy, Ayman Atia, Ahmed Mohamed Fahmy Yousef, Ahmed M. Abd El-Haleem, Mohamed G. Anany, Noha A. Elmosilhy, Abdullah I. Salama, Alaa Hamdy, Helmy M. El Zoghby, Eman Serag El Din
2021 International Mobile Intelligent and Ubiquitous Computing Conference Miucc 2021, 2021
No-regret matching game algorithm for NOMA based UAV-assisted NB-IoT systems
Samar Shaker Metwaly, Ahmed. M. Abd El-Haleem, Osama El-Ghandour
Ingenierie Des Systemes D Information, 2021
Intelligent Virtual Tutor for Online Laboratory Experiments Based on Modelling the Student's Mouse Interaction Behavior
Ayman Atia, Ahmed Mohamed Fahmy Yousef, Alaa Hamdy, Ahmed M. Abd El-Haleem, Mahmoud M. Elmesalawy
2021 IEEE 12th Annual Ubiquitous Computing Electronics and Mobile Communication Conference Uemcon 2021, 2021
Automatic Identification of Student's Cognitive Style from Online Laboratory Experimentation using Machine Learning Techniques
Ahmed Mohamed Fahmy Yousef, Ayman Atia, Amira Youssef, Noha A. Saad Eldien, Alaa Hamdy, Ahmed M. Abd El-Haleem, Mahmoud M. Elmesalawy
2021 IEEE 12th Annual Ubiquitous Computing Electronics and Mobile Communication Conference Uemcon 2021, 2021
NOMA Based Matching Game Algorithm for Narrowband Internet of Things (NB-IoT) System
Samar Metwaly, Ahmed Abd El-Haleem, Osama El-Ghandour
Ingenierie Des Systemes D Information, 2020
Matching Game-Based Cell Association in Multi-RAT HetNet Considering Device Requirements
Mohamed Anany, Mahmoud M. Elmesalawy, Ahmed M. Abd El-Haleem
IEEE Internet of Things Journal, 2019
Enhanced ANDSF WiFi discovery mechanism using machine learning for mobile data offloading
A. M. Alagrami, Mahmoud M. Elmesalawy, Ahmed M. Abd El-Haleem
Icenco 2019 2019 15th International Computer Engineering Conference Utilizing Machine Intelligence for A Better World, 2019
Soft sectored fractional frequency reuse in LTE-advanced networks
Eman AbdelNaby, Ihab A. Ali, Ahmed M.Abd El – Haleem, Hesham F. A. Hamed
Iop Conference Series Materials Science and Engineering, 2019
Optimal Deployment of Heterogeneous Wireless Nodes in Integrated LTElWi-Fi Networks
Noha A. Elmosilhy, Ahmed M. Abd El-Haleem, Mahmoud M. Elmesalawy
2019 IEEE 10th Annual Ubiquitous Computing Electronics and Mobile Communication Conference Uemcon 2019, 2019
Joint network and mode selection in 5G multi RAT heterogeneous networks
Ahmed I. Abdulshakoor, Mahmoud M. Elmesalawy, Noha A. Elmosilhy, Ahmed M. Abd El-Haleem
2019 42nd International Conference on Telecommunications and Signal Processing Tsp 2019, 2019
User Association with Mode Selection in LWA-Based Multi-RAT HetNet
Noha A. Elmosilhy, Mahmoud M. Elmesalawy, Ahmed M. Abd Elhaleem
IEEE Access, 2019
Multipath activity based routing protocol for mobile cognitive radio Ad Hoc networks
Shereen Omar, Osama El Ghandour, Ahmed M. Abd El-Haleem
Wireless Communications and Mobile Computing, 2017
TRIDNT: Isolating dropper nodes with some degree of selfishness in MANET
Ahmed M. Abd El-Haleem, Ihab A. Ali, Ibrahim I. Ibrahim, Abdel Rahman H. El-Sawy
Communications in Computer and Information Science, 2011
Trust Model for TRIDNT trust based routing protocol
Ahmed M. Abd El-Haleem, Ihab A. Ali, Ibrahim I. Ibrahim, Abdel Rahman H. El-Sawy
Icctd 2010 2010 2nd International Conference on Computer Technology and Development Proceedings, 2010

Ahmed Mostafa Abdelhaleem Abdelkhalek

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