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10 results on '"Bagaa, Miloud"'

1. 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

2. 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

3. 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

7. Content delivery network slicing

Author

Retal, Sara, Bagaa, Miloud, Taleb, Tarik, Flinck, Hannu, Department of Communications and Networking, Nokia Bell Labs, Aalto-yliopisto, and Aalto University

Abstract

openaire: EC/H2020/723172/EU//5GPagoda Content Delivery Networks (CDNs) emerged to manage the great amount of content, as well as the transmissions over long distances. In recent years, this concept proves to be a promising solution for emergent enterprises. In this paper, we present a Content Delivery Network as a Service (CDNaaS) platform which can create virtual machines (VMs) through a network of data centers and provide a customized slice of CDN to users. CDNaaS manages a great number of videos by means of caches, transcoders, and streamers hosted in different VMs. However, an optimal placement of VMs with adequate flavors for the different images is required to obtain an efficient slice of CDN. In this work, we argue the need to find a convenient slice for the CDN owner while respecting his performance requirements and minimizing as much as possible the incurred cost. We first formulate the VMs placement problem as two Linear Integer problem solutions, aiming at minimizing the cost and maximizing the quality of experience of streaming. Then,extensive simulation results are presented to illustrate the effectiveness of the proposed models.

Published
2017

8. Dynamic Cloud Resource Scheduling in Virtualized 5G Mobile Systems

Author

Taleb Tarik, Bagaa Miloud, Andras Vajda, and Ahmad Bilal

Subjects
ta113, Computer science, business.industry, Distributed computing, CPU time, 020302 automobile design & engineering, 020206 networking & telecommunications, Cloud computing, 02 engineering and technology, Service provider, Scheduling (computing), Elasticity (cloud computing), 0203 mechanical engineering, 0202 electrical engineering, electronic engineering, information engineering, Resource management, Central processing unit, Mobile telephony, business, 5G, Computer network
Abstract

In virtualized networks, network functions are delivered as software running on generic hardware allowing service providers to dynamically allocate resources based on traffic and service demands. Network Function Virtualization (NFV) is becoming a key enabler and consequently a hot research topic. Dynamic scaling of resources in NFV is a highly important challenge towards its implementation in real-life networks. In this paper, we propose a method to predict the required resources in the appropriate time to sustain true elasticity in NFV. The capacity of different Virtualized Network Functions (VNFs) would increase/decrease in a way that the CPU utilization is maximized while the overall cost is minimized. In this paper, we present two strategies to predict the day-ahead CPU utilization. The first strategy is an offline scheduling method that helps managing elasticity in virtualized networks by predicting normal days events. The second one is an online scheduling approach that predicts the day-ahead CPU utilization during sudden peaks due to some unusual circumstances. In this paper, we also present new promising results that show the correlation between the control and data planes. Finally, we propose a hybrid algorithm that uses both strategies to efficiently handle elasticity in virtualized networks. The obtained results are encouraging and are all based on real-life data of mobile operator networks.

Published
2017

9. User Mobility-Aware Virtual Network Function Placement for Virtual Mobile NetworkInfrastructure

Author

Taleb, Tarik, Bagaa, Miloud, Ksentini, Adlen, Department of Communications and Networking [Aalto Univ], School of Electrical Engineering [Aalto Univ], Aalto University-Aalto University, 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.), Dependability Interoperability and perfOrmance aNalYsiS Of networkS (DIONYSOS), 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), CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-CentraleSupélec-Télécom Bretagne-Université de Rennes 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Université de Bretagne Sud (UBS)-Centre National de la Recherche Scientifique (CNRS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA), 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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-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)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), and Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)

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

International audience

Published
2015

10. Information Security in Wireless Sensor Networks

Author

Ouadjaout, Abdelraouf, Bagaa, Miloud, Bachir, Abdelmalik, Challal, Yacine, Lasla, Nouredine, Khelladi, Lyes, Laboratoire des Systèmes Informatiques (LSI), Université des Sciences et de la Technologie Houari Boumediene [Alger] (USTHB), Environnements et outils pour le Génie Logiciel Industriel (ADELE), Laboratoire d'Informatique de Grenoble (LIG), Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre Mendès France - Grenoble 2 (UPMF)-Université Joseph Fourier - Grenoble 1 (UJF), Heuristique et Diagnostic des Systèmes Complexes [Compiègne] (Heudiasyc), Université de Technologie de Compiègne (UTC)-Centre National de la Recherche Scientifique (CNRS), Institut National de formation en Informatique (INI), Institut National de formation en Informatique, Centre de recherche sur l'Information Scientifique et Technique (CERIST), and Ministère de l'Education nationale, de l’Enseignement supérieur et de la Recherche (M.E.N.E.S.R.)

Subjects
020203 distributed computing, [INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI], denial of service countermeasures, energy exhaustion attacks, 0202 electrical engineering, electronic engineering, information engineering, data dissemination security, data aggregation security, 020206 networking & telecommunications, 02 engineering and technology, Key management
Abstract

In this chapter, we provide a comprehensive survey of security issues in wireless sensor networks. We show that the main features of WSNs, namely their limited resources, wireless communications, and close physical coupling with environment, are the main causes of the their security vulnerabilities. We discuss the main attacks stemming from these vulnerabilities, along with the solutions proposed in the literature to cope with them. The security solutions are analyzed with respect to the different layers of the network protocol stack and cover the following issues: Key management, secure data dissemination, secure data aggregation, secure channel access and secure node compromise.

Published
2009

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