31 results on '"Piamrat, Kandaraj"'
Search Results
2. Network traffic analysis using machine learning: an unsupervised approach to understand and slice your network
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Aouedi, Ons, Piamrat, Kandaraj, Hamma, Salima, and Perera, J. K. Menuka
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- 2022
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3. Federated Learning for intrusion detection system: Concepts, challenges and future directions
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Agrawal, Shaashwat, Sarkar, Sagnik, Aouedi, Ons, Yenduri, Gokul, Piamrat, Kandaraj, Alazab, Mamoun, Bhattacharya, Sweta, Maddikunta, Praveen Kumar Reddy, and Gadekallu, Thippa Reddy
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- 2022
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4. Handling partially labeled network data: A semi-supervised approach using stacked sparse autoencoder
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Aouedi, Ons, Piamrat, Kandaraj, and Bagadthey, Dhruvjyoti
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- 2022
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5. AD3-GLaM: A cooperative distributed QoE-based approach for SVC video streaming over wireless mesh networks
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Pham, Tran Anh Quang, Singh, Kamal Deep, Rodríguez-Aguilar, Juan Antonio, Picard, Gauthier, Piamrat, Kandaraj, Cerquides, Jesús, and Viho, César
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- 2018
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6. Efficient queuing scheme through cross-layer approach for multimedia transmission over WSNs
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Bennis, Ismail, Fouchal, Hacène, Piamrat, Kandaraj, and Ayaida, Marwane
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- 2018
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7. Empowering Digital Twin for Future Networks with Graph Neural Networks: Overview, Enabling Technologies, Challenges, and Opportunities.
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Ngo, Duc-Thinh, Aouedi, Ons, Piamrat, Kandaraj, Hassan, Thomas, and Raipin-Parvédy, Philippe
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DIGITAL twins ,SOFTWARE-defined networking ,MULTICASTING (Computer networks) ,NETWORK performance ,COMPUTER network security ,5G networks ,PROBLEM solving - Abstract
As the complexity and scale of modern networks continue to grow, the need for efficient, secure management, and optimization becomes increasingly vital. Digital twin (DT) technology has emerged as a promising approach to address these challenges by providing a virtual representation of the physical network, enabling analysis, diagnosis, emulation, and control. The emergence of Software-defined network (SDN) has facilitated a holistic view of the network topology, enabling the use of Graph neural network (GNN) as a data-driven technique to solve diverse problems in future networks. This survey explores the intersection of GNNs and Network digital twins (NDTs), providing an overview of their applications, enabling technologies, challenges, and opportunities. We discuss how GNNs and NDTs can be leveraged to improve network performance, optimize routing, enable network slicing, and enhance security in future networks. Additionally, we highlight certain advantages of incorporating GNNs into NDTs and present two case studies. Finally, we address the key challenges and promising directions in the field, aiming to inspire further advancements and foster innovation in GNN-based NDTs for future networks. [ABSTRACT FROM AUTHOR]
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- 2023
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8. Data Analysis for Self-Driving Vehicles in Intelligent Transportation Systems
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Park, Hyunhee, Piamrat, Kandaraj, Singh, Kamal, and Chen, Hsing-Chung
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Ferry services ,Driverless cars - Abstract
Self-driving vehicles are regarded as the future of transportation. In the near future, self-driving vehicles would ferry passengers from one place to another place, like driverless taxis, and transport packages [...]
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- 2020
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9. QoE-based routing algorithms for H.264/SVC video over ad-hoc networks
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Pham, Tran Anh Quang, Piamrat, Kandaraj, Singh, Kamal Deep, and Viho, César
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- 2016
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10. Radio resource management in emerging heterogeneous wireless networks
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Piamrat, Kandaraj, Ksentini, Adlen, Bonnin, Jean-Marie, and Viho, César
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- 2011
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11. Federated Semisupervised Learning for Attack Detection in Industrial Internet of Things.
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Aouedi, Ons, Piamrat, Kandaraj, Muller, Guillaume, and Singh, Kamal
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Security has become a critical issue for Industry4.0 due to different emerging cyber-security threats. Recently, many deep learning (DL) approaches have focused on intrusion detection. However, such approaches often require sending data to a central entity. This in turn raises concerns related to privacy, efficiency, and latency. Despite the huge amount of data generated by the Internet of Things (IoT) devices in Industry 4.0, it is difficult to get labeled data, because data labeling is costly and time-consuming. This poses many challenges for several DL approaches, which require labeled data. In order to deal with these issues, new approaches should be adopted. This article proposes a novel federated semisupervised learning scheme that takes advantage of both unlabeled and labeled data in a federated way. First, an autoencoder (AE) is trained on each device (using unlabeled local/private data) to learn the representative and low-dimensional features. Then, a cloud server aggregates these models into a global AE using federated learning (FL). Finally, the cloud server composes a supervised neural network, by adding fully connected layers (FCN) to the global encoder (the first part of the global AE) and trains the resulting model using publicly available labeled data. Extensive case studies on two real-world industrial datasets demonstrate that our model: (a) ensures that no local private data is exchanged; (b) detects attacks with high classification performance, (c) works even when only a few amounts of labeled data are available; and (d) haslow communication overhead. [ABSTRACT FROM AUTHOR]
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- 2023
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12. Network Feature Selection based on Machine Learning for Resource Management
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Aouedi, Ons, Piamrat, Kandaraj, Parrein, Benoît, Laboratoire des Sciences du Numérique de Nantes (LS2N), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), and Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)
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[INFO]Computer Science [cs] - Abstract
International audience; Resource management in SDN (e.g. network slicing) is an emerging area that attracts the attention of academia and industry. It is an indispensable technology in 5G systems. To effectively manage and optimize network resources, more intelligence needs to be deployed. Therefore, combining real network data and Machine Learning (ML) with the benefits of SDN can be a promising solution to manage the network resources in an automated and intelligent way. However, a real network dataset can have redundant and unneeded features. Also, ML algorithms are as good as the quality of data and the SDN is a time-critical system that requires real-time processing and decision. Thus, data preprocessing is a necessary task, which helps to keep the relevant features and makes the prediction quicker and more accurate.This work presents a comparative analysis between two feature selection methods, which are Recursive Feature Elimination (RFE) and Information Gain Attribute Evaluation (InfoGain), using several classifiers on different reduced versions of the network’s dataset.
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- 2020
13. Resource Management in Wireless Access Networks: A layer-based classification - Version 1.0
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Pham, Quang Tran Anh, Piamrat, Kandaraj, Viho, César, 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), 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), 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), Centre de Recherche en Sciences et Technologies de l'Information et de la Communication - EA 3804 (CRESTIC), Université de Reims Champagne-Ardenne (URCA), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-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 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 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-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)-Université de Rennes (UNIV-RENNES)-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)
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[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] - Abstract
In recent years, wireless access networks have recently experienced significant breakthrough that strongly impacted the way of managing network resources. We can observe considerable emergence of heterogeneous wireless networks where different types of wireless technologies coexist in the same region. Beside widespread deployment of heterogeneous wireless networks, the progress of electronic devices enables implementation of multiple radio interfaces on a single mobile device. Consequently it requires new resource management approaches to exploit diversity gain. Moreover, the dramatic increase in traffic leads to a necessity of efficient resource management. As a result, numerous studies have been conducted to address various issues in resource management. In this report, a survey of recent resource management solutions is presented. It is a layer-based classification survey that can be helpful for any researcher that wants to know the state of the art on existing resource management schemes as well as for technical experts who wants to apply them to real devices.; La percée fulgurante des réseaux d'accès sans-fil au cours de ces dernières années ont eu un impact sur la manière de gérer les ressources dans les réseaux. On peut observer l'émergence des réseaux sans-fil hétérogènes où coexistent différents types de technologies sans-fil. Parallèlement au déploiement généralisé des réseaux sans-fil hétérogènes, l'évolution des appareils électroniques fait que l'on peut avoir plusieurs interfaces radios sur n'importe quel terminal mobile. De nouvelles approches de gestion de ressources sont donc nécessaires pour exploiter cette diversité. Par ailleurs, l'augmentation du trafic oblige à une gestion efficace des ressources des réseaux sans-fil. Ce rapport propose un état de l'art des travaux qui ont été menés pour traiter les différents problèmes de gestion de ressource. C'est une classification en couches qui peut servir à tout chercheur souhaitant connaître l'état de l'art en matière de gestion de ressources dans les réseaux sans-fil ainsi qu'á des experts techniques qui veulent les implémenter.
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- 2014
14. Quality-aware Resource Management inWireless Networks
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Piamrat, Kandaraj, 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), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-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 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 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-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), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-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), Université Rennes 1, César VIHO(cesar.viho@irisa.fr), 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)
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[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,Wireless Networks ,Heterogeneous Networks ,Réseaux sans-fil ,Multimedia Applications ,Application multimédia ,Resource Management ,Gestion de ressources ,Réseaux hétérogènes ,Qualité d'Expérience ,Quality of Experience - Abstract
Wireless multimedia networking is gaining tremendous success nowadays. Due to their characteristics (limited bandwidth, variable radio conditions, greater interference, etc.), the need of more efficient management has become crucial. Meanwhile, traditional ways of managing network, using information from monitoring technical parameters (loss, delays, jitter, etc.), fail to give accurate evaluations of user experience or Quality of Experience (QoE). In this thesis, new methods based on QoE indicator have been proposed to solve these problems. The propositions are admission control, rate adaptation, and packet scheduling regarding network operator as well as network selection regarding user side. The real-time measurement of QoE is accomplished with PSQA (Pseudo-Subjective Quality Assessment) tool. The simulations have been conducted using different wireless technologies both in homogeneous and heterogeneous environment. The obtained results encourage the use of QoE concept in further research, which could pave the road to a new paradigm of resource management.; Les applications multimédias pour terminaux mobiles connaissent un succès grandissant. Cela oblige à développer de nouvelles méthodes plus efficaces de gestion des ressources des réseaux sans-fil du fait de leurs caractéristiques particulières : bande-passante limitée, état radio variable, interférences plus importantes, etc. Par ailleurs, les méthodes classiques de la gestion de ressources basées sur des paramètres techniques (perte/retard de paquets, gigue, etc.) ne parviennent pas à donner des évaluations précises de la qualité telle que perçue (encore appelée Qualité d'Expérience ou QdE) par l'utilisateur de ces applications. Cette thèse s'appuie sur une technique hybride nommée PSQA (Pseudo-Subjective Quality Assessment) d'évaluation pseudo-subjective en temps réel de la QdE pour proposer de nouvelles méthodes de gestion de ressources dans les réseaux multimédias sans-fil. Que ce soit du côté de l'opérateur réseau ou du côté de l'utilisateur, nous avons proposé des méthodes de contrôle d'accès et d'ordonnancement ainsi que des méthodes de sélection de réseaux d'accès dans le contexte des réseaux sans-fil hétérogènes utilisant différentes technologies (IEEE 802.11, UMTS, etc.). Les résultats obtenus encouragent l'utilisation du concept de QdE et ouvre la voie à un nouveau paradigme dans la gestion des ressources dans les réseaux multimédias sans-fil.
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- 2010
15. QoE-aware Network Selection in Wireless Heterogeneous Networks
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Piamrat, Kandaraj, Viho, César, Ksentini, Adlen, Bonnin, Jean-Marie, 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), 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), 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), Département Réseaux, Sécurité et Multimédia (RSM), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), INRIA, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-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 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 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-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)-Université de Rennes (UNIV-RENNES)-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)
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[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] - Abstract
Deployment of next-generation network (4G) begins to spread throughout the world. With variety of network technologies and QoS restrictions on emerging applications; it is more difficult for users to select the best access network to request for connection. Even though many schemes have been proposed in the literature but none of them takes into account quality of experience (QoE) perceived by user for making decision. As QoE represents perception experienced by the user, it is thus an essential indicator for network evaluation, especially with multimedia communications nowadays. Therefore, in this paper we propose a novel network selection mechanism that takes quality of experience into consideration for decision making. It is a user-based and network-assisted approach thus a compromise solution between user and network benefit. The main idea is to use quality of experience of ongoing users in candidate networks as an indicator to select the best network for connection. We have implemented and tested our mechanism in network simulator NS-2. The obtained results illustrate that even with a simple mechanism; we can significantly improve QoE of mobile node and load balancing between networks.
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- 2010
16. Rate Adaptation Mechanisms for Multimedia Multicasting in Wireless IEEE 802.11 Networks
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Piamrat, Kandaraj, Viho, César, Ksentini, Adlen, Bonnin, Jean-Marie, 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), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-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 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 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-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), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-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), Département Réseaux, Sécurité et Multimédia (RSM), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), 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)
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[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,Wireless Networks ,Multicast ,ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS ,Rate Adaptation Mechanism ,Video Streaming ,Data_CODINGANDINFORMATIONTHEORY ,Quality of Experience - Abstract
The rising number of wireless users and terminals has pushed the deployment of many applications over wireless networks. Video multicasting is one of them. Wireless multicast has a great benefit in terms of resource utilization. Due to wireless nature of the media, a packet that is sent only once can reach all recipients. However, multicasting lacks in feed back mechanism. This makes it hard to deal with reliability or quality of service. Moreover, in order to reach all nodes especially the farther ones, multicast is always sent at the basic rate of 1 or 2 Mbps. This low rate may penalize other traffics and waste bandwidth capacity because of longer channel occupancy. As IEEE 802.11 standard provides possibility of multi-rate transmission, we propose to adapt multicast transmission rate according to quality of experience perceived at multicast users. We illustrate the significant performance improvement obtained with our scheme comparing to other existing schemes.
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- 2009
17. Resource Management in Mobile Heterogeneous Networks: State of the Art and Challenges
- Author
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Piamrat, Kandaraj, Viho, César, Ksentini, Adlen, Bonnin, Jean-Marie, 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), 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), 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), Département Réseaux, Sécurité et Multimédia (RSM), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), INRIA, Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-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 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 1 (UR1), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-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), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-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), 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), and Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)
- Subjects
radio resource management ,[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,bandwidth allocation ,[INFO.INFO-OH]Computer Science [cs]/Other [cs.OH] ,network selection ,quality of service ,architectural design ,4G networks ,ACM: C.: Computer Systems Organization/C.2: COMPUTER-COMMUNICATION NETWORKS ,mobility - Abstract
Deployment of next-generation networks (i.e. 4G) begins to spread throughout the world. Today's emerging multimedia application has many requirements in terms of quality of service and users always want to be best connected anywhere, anytime, and anyhow. To satisfy these demands, a variety of access technologies has become available: WiFi (Wireless Fidelity), WiMAX (Worldwide Interoperability for Microwave Access), and Cellular networks. This has made it difficult for service provider to select the best network for requesting services and to control the quality level of ongoing connections. Thus, the use of resources management to prevent overloaded or underutilized networks as well as to best satisfy users is indispensable. This report addresses the state of the art on radio resource management in next-generation networks. Recent schemes in network selection and bandwidth allocation are discussed in several aspects, namely decision making, QoS, mobility, and architectural design.
- Published
- 2008
18. Q-RoSA: QoE-aware routing for SVC video streaming over ad-hoc networks.
- Author
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Pham Tran Anh Quang, Piamrat, Kandaraj, Singh, Kamal Deep, and Viho, Cesar
- Published
- 2016
- Full Text
- View/download PDF
19. Q-SWiM: QoE-based routing algorithm for SVC video streaming over wireless mesh networks.
- Author
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Quang, Pham Tran Anh, Piamrat, Kandaraj, Deep Singh, Kamal, and Viho, Cesar
- Published
- 2016
- Full Text
- View/download PDF
20. Video Streaming Over Ad Hoc Networks: A QoE-Based Optimal Routing Solution.
- Author
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Quang, Pham Tran Anh, Piamrat, Kandaraj, Singh, Kamal Deep, and Viho, Cesar
- Subjects
- *
STREAMING video & television , *AD hoc computer networks , *ROUTING algorithms , *QUALITY control , *BANDWIDTH allocation , *ELECTRONIC linearization - Abstract
Multimedia streaming over multihop ad hoc networks is an emerging application. Consequently, a novel routing algorithm designed for video streaming is necessary. Existing video routing solutions focus on technical parameters such as bandwidth, jitter, delay, etc. However, these parameters are not perfectly correlated to the quality of experience (QoE) perceived by users. In this paper, we study a QoE-based routing problem in multihop wireless ad hoc networks. We use the pseudosubjective quality assessment (PSQA) tool to derive the mean opinion score (MOS). As PSQA is an implicit nonlinear function, we use a linearization method to derive its approximate mathematical form. Then, we formulate the optimal QoE-based routing problem as a mixed-integer linear programming. We show major properties of feasible solutions of the problem and then exploit them to propose a heuristic algorithm. The results show that the proposed algorithm can enhance resource utilization and the users' experience with wireless networks. Moreover, the feasible solution can be reached in real time using the proposed heuristic approach. [ABSTRACT FROM PUBLISHER]
- Published
- 2017
- Full Text
- View/download PDF
21. QoE-Aware Routing for Video Streaming over VANETs.
- Author
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Pham, Tran Anh Quang, Piamrat, Kandaraj, and Viho, Cesar
- Published
- 2014
- Full Text
- View/download PDF
22. QoE-aware routing for video streaming over ad-hoc networks.
- Author
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Quang, Pham Tran Anh, Piamrat, Kandaraj, and Viho, Cesar
- Published
- 2014
- Full Text
- View/download PDF
23. Optimising QoE for Scalable Video multicast over WLAN.
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Deep Singh, Kamal, Piamrat, Kandaraj, Park, Hyunhee, Viho, Cesar, and Bonnin, Jean-Marie
- Published
- 2013
- Full Text
- View/download PDF
24. Managing wireless IPTV in multimedia home networking.
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Piamrat, Kandaraj, Fontaine, Patrick, and Viho, Cesar
- Published
- 2013
25. CLAP.
- Author
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Piamrat, Kandaraj and Fontaine, Patrick
- Published
- 2011
- Full Text
- View/download PDF
26. Coordinated architecture for wireless home networks.
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Piamrat, Kandaraj and Fontaine, Patrick
- Published
- 2011
- Full Text
- View/download PDF
27. QoE-Aware Scheduling for Video-Streaming in High Speed Downlink Packet Access.
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Piamrat, Kandaraj, Singh, Kamal Deep, Ksentini, Adlen, Viho, Cesar, and Bonnin, Jean-Marie
- Published
- 2010
- Full Text
- View/download PDF
28. Punishment Protocol for Backoff Manipulation in MAC IEEE 802.11.
- Author
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Piamrat, Kandaraj
- Published
- 2006
- Full Text
- View/download PDF
29. Intelligent Traffic Management in Next-Generation Networks.
- Author
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Aouedi, Ons, Piamrat, Kandaraj, and Parrein, Benoît
- Subjects
NEXT generation networks ,DEEP learning ,MACHINE learning ,ANOMALY detection (Computer security) ,FEATURE selection ,SMART devices - Abstract
The recent development of smart devices has lead to an explosion in data generation and heterogeneity. Hence, current networks should evolve to become more intelligent, efficient, and most importantly, scalable in order to deal with the evolution of network traffic. In recent years, network softwarization has drawn significant attention from both industry and academia, as it is essential for the flexible control of networks. At the same time, machine learning (ML) and especially deep learning (DL) methods have also been deployed to solve complex problems without explicit programming. These methods can model and learn network traffic behavior using training data/environments. The research community has advocated the application of ML/DL in softwarized environments for network traffic management, including traffic classification, prediction, and anomaly detection. In this paper, we survey the state of the art on these topics. We start by presenting a comprehensive background beginning from conventional ML algorithms and DL and follow this with a focus on different dimensionality reduction techniques. Afterward, we present the study of ML/DL applications in sofwarized environments. Finally, we highlight the issues and challenges that should be considered. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
30. Big Data: An incoming challenge for vehicular ad‐hoc networking.
- Author
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Bourdy, Emilien, Piamrat, Kandaraj, and Herbin, Michel
- Published
- 2019
- Full Text
- View/download PDF
31. Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions.
- Author
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Aouedi O, Sacco A, Piamrat K, and Marchetto G
- Subjects
- Humans, Algorithms, Databases, Factual, Genomics, Privacy, Internet of Things
- Abstract
Recent medical applications are largely dominated by the application of Machine Learning (ML) models to assist expert decisions, leading to disruptive innovations in radiology, pathology, genomics, and hence modern healthcare systems in general. Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated. Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects. This paper sheds light upon the emerging deployment of FL, provides a broad overview of current approaches and existing challenges, and outlines several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.
- Published
- 2023
- Full Text
- View/download PDF
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