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237 results on '"Bagaa, Miloud"'

4. Key Innovations in ANASTACIA: Advanced Networked Agents for Security and Trust Assessment in CPS/IOT Architectures

Author

Bernabe, Jorge Bernal, primary, Molina, Alejandro, additional, Skarmeta, Antonio, additional, Bianchi, Stefano, additional, Cambiaso, Enrico, additional, Vaccari, Ivan, additional, Scaglione, Silvia, additional, Aiello, Maurizio, additional, Trapero, Rubén, additional, Bouet, Mathieu, additional, Belabed, Dallal, additional, Bagaa, Miloud, additional, Addad, Rami, additional, Taleb, Tarik, additional, Rivera, Diego, additional, Mady, Alie El-Din, additional, Rodriguez, Adrian Quesada, additional, Crettaz, Cédric, additional, Ziegler, Sébastien, additional, Kim, Eunah, additional, Filipponi, Matteo, additional, Bajic, Bojana, additional, Garcia-Carrillo, Dan, additional, and Marin-Perez, Rafael, additional

Published
2022
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6. Enhancing Autonomous Driving Navigation Using Soft Actor-Critic.

Author

Elallid, Badr Ben, Benamar, Nabil, Bagaa, Miloud, and Hadjadj-Aoul, Yassine

Subjects
REINFORCEMENT learning, DEEP reinforcement learning, CONVOLUTIONAL neural networks, ALGORITHMS, ACADEMIA, TRAFFIC circles
Abstract

Autonomous vehicles have gained extensive attention in recent years, both in academia and industry. For these self-driving vehicles, decision-making in urban environments poses significant challenges due to the unpredictable behavior of traffic participants and intricate road layouts. While existing decision-making approaches based on Deep Reinforcement Learning (DRL) show potential for tackling urban driving situations, they suffer from slow convergence, especially in complex scenarios with high mobility. In this paper, we present a new approach based on the Soft Actor-Critic (SAC) algorithm to control the autonomous vehicle to enter roundabouts smoothly and safely and ensure it reaches its destination without delay. For this, we introduce a destination vector concatenated with extracted features using Convolutional Neural Networks (CNN). To evaluate the performance of our model, we conducted extensive experiments in the CARLA simulator and compared it with the Deep Q-Network (DQN) and Proximal Policy Optimization (PPO) models. Qualitative results reveal that our model converges rapidly and achieves a high success rate in scenarios with high traffic compared to the DQN and PPO models. [ABSTRACT FROM AUTHOR]

Published
2024
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9. Energy-Efficient RIS-Enabled SISO-OFDMA Communication via Lower Bound Optimization.

Author

Bidabadi, Samaneh, Ouameur, Messaoud Ahmed, Bagaa, Miloud, and Massicotte, Daniel

Subjects
SPECTRUM allocation, REFLECTANCE, ENERGY consumption, TELECOMMUNICATION systems, POWER transmission
Abstract

The pursuit of energy-efficient solutions in the context of reconfigurable intelligent surface (RIS)-assisted wireless networks has become imperative and transformative. This paper investigates the integration of RIS into an orthogonal frequency-division multiple access (OFDMA) framework for multi-user downlink communication systems. We address the challenge of jointly optimizing RIS reflection coefficients alongside OFDMA frequency and power allocations, with the aim of maximizing energy efficiency. This optimization is subject to specific quality-of-service (QoS) requirements for each user equipment (UE) and a constraint on transmission power and the RIS phase shift matrix. To address this complex optimization problem, we propose a novel practical and low-complexity approach that is based on optimizing a computationally efficient and numerically tractable lower bound on energy efficiency. The numerical results highlight the effectiveness of our approach, demonstrating a substantial increase in energy efficiency compared to scenarios without RIS, with random RIS integration, and with the scheme using the Genetic Algorithm (GA). [ABSTRACT FROM AUTHOR]

Published
2024
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17. SMART: Secure Multi-pAths Routing for wireless sensor neTworks

Author

Lasla, Noureddine, Derhab, Abdelouahid, Ouadjaout, Abdelraouf, Bagaa, Miloud, Challal, Yacine, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Guo, Song, editor, Lloret, Jaime, editor, Manzoni, Pietro, editor, and Ruehrup, Stefan, editor

Published
2014
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28. Semi-invertible Convolutional Neural Network for Overall Survival Prediction in Head and Neck Cancer

Author

Khelifa, Saif Eddine, Khelladi, Lyes, Bagaa, Miloud, Hadjadj-Aoul, Yassine, Université des Sciences et de la Technologie Houari Boumediene = University of Sciences and Technology Houari Boumediene [Alger] (USTHB), Centre de recherche sur l'Information Scientifique et Technique (CERIST), Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.), Aalto University, mEasuRing and ManagIng Network operation and Economic (ERMINE), Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-RÉSEAUX, TÉLÉCOMMUNICATION ET SERVICES (IRISA-D2), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]
Abstract

International audience; The paper addresses the issue of overall survival prediction in head and neck cancer as an effective mean of improving clinical diagnosis and treatment planning. A new solution is proposed using semi-invertible convolutional networks. Our model exploits the 3D features of computed tomography (CT) scans to enrich the dataset used in the learning phase and thereby improve the prediction accuracy. This is achieved by designing a first architecture featuring a combination of a CNN classifier with a fully convolutional network pre-processor. The latter has been replaced in the second solution by an invertible network to deal with the memory constraints noticed in the first architecture. Obtained results showed that both architectures have led to considerable improvements in terms of prediction accuracy (0.75) compared to state-of-the-art solutions.

Published
2022

40. Collaborative Cross System AI

Author

Bagaa, Miloud, Taleb, Tarik, Riekki, Jukka, Song, Jae Seung, Department of Communications and Networking, Mobile Network Softwarization and Service Customization, University of Oulu, Sejong University, Aalto-yliopisto, and Aalto University

Abstract

Funding Information: AcknoWledgment The research leading to these results received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement no. 101016509 (project CHARITY). The article reflects only the authors’ views. The Commission is not responsible for any use that may be made of the information it contains. The research work is also partially funded by the Academy of Finland 6Genesis project under Grant No. 318927, and by the Academy of Finland CSN project under Grant No. 311654. Prof. Song was supported by the Institute of Information & Communications Technology Planning & Evaluation (IITP) (No. 2020-0-00959). Dr. Miloud Bagaa work was supported by the CSN project. Publisher Copyright: © 1986-2012 IEEE. The emerging industrial verticals set new challenges for 5G and beyond systems. Indeed, the heterogeneity of the underlying technologies and the challenging and conflicting requirements of the verticals make the orchestration and management of networks complicated and challenging. Recent advances in network automation and artificial intelligence (AI) create enthusiasm from industries and academia toward applying these concepts and techniques to tackle these challenges. With these techniques, the network can be autonomously optimized and configured. This article suggests a collaborative cross-system AI that leverages diverse data from different segments involved in the end-to-end communication of a service, diverse AI techniques, and diverse network automation tools to create a self-optimized and self-orchestrated network that can adapt according to the network state. We align the proposed framework with the ongoing network standardization.

Published
2021

45. mMTC Deployment over Sliceable Infrastructure

Author

Motlagh, Naser Hossein, Afolabi, Ibrahim, Pozza, Matteo, Bagaa, Miloud, Taleb, Tarik, Tarkoma, Sasu, Flinck, Hannu, University of Helsinki, Department of Communications and Networking, Nokia Bell Labs, Aalto-yliopisto, and Aalto University

Subjects
Sensors, 5G mobile communication, Air pollution, Cloud computing, Intelligent sensors, Network slicing, Wireless sensor networks
Abstract

Publisher Copyright: IEEE Massive Machine Type Communication (mMTC) has long been identified as a major vertical sector and enabler of the industry 4.0 technological evolution that will seamlessly ease the dynamics of machine-to-machine communications while leveraging 5G technology. To advance this concept, we have developed and tested an mMTC network slice called Megasense. Megasense is a complete framework that consists of multiple software modules, which is used for collecting and analyzing air pollution data that emanates from a massive amount of air pollution sensors. Taking advantage of 5G networks, Megasense will significantly benefit from an underlying communication network that is traditionally elastic and can accommodate the on-demand changes in requirements of such a use case. As a result, deploying the sensor nodes over a sliceable 5G system is deemed the most appropriate in satisfying the resource requirements of such a use case scenario. In this light, in order to verify how 5G-ready our Megasense solution is, we deployed it over a network slice that is totally composed of virtual resources. We have also evaluated the impact of the network slicing platform on Megasense in terms of bandwidth and resource utilization. We further tested the performances of the Megasense system and come up with different deployment recommendations based on which the Megasense system would function optimally.

Published
2021

46. Optimization of Flow Allocation in Asynchronous Deterministic 5G Transport Networks by Leveraging Data Analytics

Author

Prados-Garzon, Jonathan, Taleb, Tarik, and Bagaa, Miloud

Abstract

Time-Sensitive Networking (TSN) and Deterministic Networking (DetNet) technologies are increasingly recognized as key levers of the future 5G transport networks (TNs) due to their capabilities for providing deterministic Quality-of-Service and enabling the coexistence of critical and best-effort services. Additionally, they rely on programmable and cost-effective Ethernet-based forwarding planes. This article addresses the flow allocation problem in 5G backhaul networks realized as asynchronous TSN networks, whose building block is the Asynchronous Traffic Shaper. We propose an offline solution, dubbed “Next Generation Transport Network Optimizer” (NEPTUNO), that combines exact optimization methods and heuristic techniques and leverages data analytics to solve the flow allocation problem. NEPTUNO aims to maximize the flow acceptance ratio while guaranteeing the deterministic Quality-of-Service requirements of the critical flows. We carried out a performance evaluation of NEPTUNO regarding the degree of optimality, execution time, and flow rejection ratio. Furthermore, we compare NEPTUNO with a novel online baseline solution for two different optimization goals. Online methods compute the flow's allocation configuration right after the flow arrives at the network, whereas offline solutions like NEPTUNO compute a long-term configuration allocation for the whole network. Our results highlight the potential of data analytics for the self-optimization of the future 5G TNs.

Published
2023
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