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