, 2.2.5 Drone's fleet communication architectures, 2.2.4 Fleet Control Strategies, p.46

, Dependability Concept, pp.48-50

, Safety Analysis techniques, p.52

|. Contents and . Chapter,

, Markov Chain (MC), p.81

, Communication reliability model for drones' networks, p.90

, Comparison between Absorbing Markov chain and Fault Tree Analysis, p.93

, 2. Future Works, p.123

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, Etant plus connus sous le nom des drones, ils sont le plus souvent utilisés dans les domaines civiles et militaires. Ils sont employés pour : la lutte contre les incendies, le sauvetage ainsi que dans des applications spécifiques comme la surveillance et l'attaque. Le vol en formation est de loin le plus utilisé car il permet une répartition judicieuse des tâches et améliore grandement l'efficacité des drones (principe de l'attaque en meute, des animaux carnassiers). Cela pose alors la problématique de la coordination et de la stratégie, Les véhicules aériens sans pilote (UAVs), utilisés et développés pour la première fois dans le domaine militaire, ont connu de profonds changements ces dernières années et sont de plus en plus utilisés dans le domaine civil

, Une performance globale de la flotte des drones doit être garantie, malgré une possible dégradation des composants ou de toute modification du réseau et de l'environnement. Il est nécessaire de détecter les comportements anormaux pouvant contribuer aux collisions et ainsi affecter la mission. Compte tenu des performances et du coût, les systèmes à tolérance de pannes et à redondance ne représentent pas toujours la solution la plus efficace pour ce type de vol de flotte en formation. Différentes méthodes telles que l'analyse par arbre de défaillance (ADD), l'analyse des modes de défaillance, L'utilisation accrue de ces systèmes coopératifs dans des environnements dangereux rend leur fiabilité essentielle pour prévenir tout événement catastrophique

, une méthode statique basée sur l'ADD est proposée, pour assurer la fiabilité de la communication entre les drones d'un côté et entre les drones et la station de base de l'autre côté en accentuant l'échange de flux d'informations. Nous utilisons des arbres de défaillance pour représenter les différentes conditions d

, nous analysons les différents états de défaillance des communications et leurs probabilités. Ce processus étant stochastique, une approche par chaîne de Markov absorbante est développée. L'approche proposée peut être utilisée pour trouver les scenarios les plus risqués et les éléments à prendre en compte pour améliorer la fiabilité

. Enfin and . Dans-une-troisième-partie, nous étudions le problème de réception des messages d'un drone en proposant un protocole basé sur le nombre de retransmissions. La réception est assurée avec une certaine probabilité de fiabilité, en fonction de plusieurs attributs tels que la modulation