Machine learning-based routing approach in UAV networks / Nadjia Tabet  

Public ISBD Titre : Machine learning-based routing approach in UAV networks Type de document : texte imprimé Auteurs : Nadjia Tabet ; Raoudha Chaabi ; Medani,Khedidja, Directeur de thèse Editeur : Setif:UFA Année de publication : 2023 Importance : 1 vol. (45 f.) Format : 29 cm Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : FANETs  UAVs  Routing  Machine learning  Q-Learning  NS3 Index. décimale : 004 Informatique Résumé : Flying Ad Hoc Networks (FANETs) have experienced significant advancements, primarily driven by the collaboration among different Unmanned Aerial Vehicles (UAVs) operating in an ad hoc manner. These networks offer a wide array of applications and services, both in civil and military domains. Establishing efficient communication is one of the most crucial issues in UAV networks, because it ensures the cooperation and the collaboration between these UAVs. Consequently, several routing strategies have been developed. However, implementing routing protocols in UAVs networks poses a challenge for researchers due to the limited energy resources, network’s inherent high mobility and rapid topology changes. Recently, machine learning-based techniques become one of the most promising solutions employed to develop intelligent routing approaches adapting to the frequent network changes. In this work, we investigate utilizing Q-Learning algorithm as a foundation in the context of UAVs networks and propose an improvement to enhance the efficiency of the routing algorithm. The implementation of the proposed protocol has simulated using the Network Simulator 3 (NS3), and the outcomes demonstrate its effectiveness in terms of energy consumption, packet delivery ration and average end to end delay Côte titre : MAI/0743 En ligne : https://drive.google.com/file/d/1_-rpDROZeJ-RBIIn-9p16N4P-DR8i_h9/view?usp=drive [...] Format de la ressource électronique : pdf Machine learning-based routing approach in UAV networks [texte imprimé] / Nadjia Tabet ; Raoudha Chaabi ; Medani,Khedidja, Directeur de thèse . - [S.l.] : Setif:UFA, 2023 . - 1 vol. (45 f.) ; 29 cm. Langues : Français (fre) Catégories : Thèses & Mémoires:Informatique Mots-clés : FANETs  UAVs  Routing  Machine learning  Q-Learning  NS3 Index. décimale : 004 Informatique Résumé : Flying Ad Hoc Networks (FANETs) have experienced significant advancements, primarily driven by the collaboration among different Unmanned Aerial Vehicles (UAVs) operating in an ad hoc manner. These networks offer a wide array of applications and services, both in civil and military domains. Establishing efficient communication is one of the most crucial issues in UAV networks, because it ensures the cooperation and the collaboration between these UAVs. Consequently, several routing strategies have been developed. However, implementing routing protocols in UAVs networks poses a challenge for researchers due to the limited energy resources, network’s inherent high mobility and rapid topology changes. Recently, machine learning-based techniques become one of the most promising solutions employed to develop intelligent routing approaches adapting to the frequent network changes. In this work, we investigate utilizing Q-Learning algorithm as a foundation in the context of UAVs networks and propose an improvement to enhance the efficiency of the routing algorithm. The implementation of the proposed protocol has simulated using the Network Simulator 3 (NS3), and the outcomes demonstrate its effectiveness in terms of energy consumption, packet delivery ration and average end to end delay Côte titre : MAI/0743 En ligne : https://drive.google.com/file/d/1_-rpDROZeJ-RBIIn-9p16N4P-DR8i_h9/view?usp=drive [...] Format de la ressource électronique : pdf

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
MAI/0743	MAI/0743	Mémoire	Bibliothéque des sciences	Anglais	Disponible Disponible

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