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, L'ensemble de ces modules a été codé dans une pile ICN dans le système d'exploitation RIOT, vol.58

, En particulier, pour permettre la cohabitation entre diverses stratégies d'acheminement, le préfixe /g/ est utilisé pour tout routage géographique. Ce préfixe est associé dans la FIB ICN à une entrée virtuelle qui représentent tous les voisins capables de faire du routage géographique. Le prochain saut est choisi parmi ces voisins en utilisant GPSR dans la

, Évaluation de l'acheminement géographique Pour évaluer une stratégie d'acheminement, deux critères principaux sont utilisés : la faisabilité (en termes de mémoire et de calcul sur les capteurs) et l'efficacité

, En particulier, le modèle est construit pour deux stratégies principales : l'acheminement géogra-phique (telle qu'introduit dans SLICT), et l'acheminement de type "inonde-etapprends, Ces coûts sont calculés à l'aide d'un modèle mathématique

-. Inonde,

L. , Inonde-et-apprends" (I&A) est l'approche la plus fréquem-ment trouvée dans la littérature ICN-IoT, vol.24, pp.134-136

, La route est apprise grâce à la réponse au dit Intérêt : le voisin qui transmet le paquet Donnée correspondant (i.e., portant le même nom ICN) est enregistré comme prochain saut pour ce nom. Pour compenser la naïveté de l'approche inonde-et-apprends, une version optimisée du processus d'inondation est aussi considérée. Elle utilise l'algorithme MPR (multi-point relais) [137] qui permet d'optimiser l'inondation en ne choi, Elle repose sur l'inondation du réseau pour des Intérêt ICN pour lesquels aucune route n'existe

, Un modèle mathématique est proposé pour représenter la performance des trois stratégies considérées : GPSR, I&A, et MPR. Ce modèle dépend de trois variables principales : le taille du réseau (le nombre de noms ICN à supporter)

, Il est construit en deux parties : la première modélise le coût de relai d'un Intérêt pour un noeud donné en fonction de la stratégie d'acheminement, et la deuxième regarde le coût global d'acheminement en étudiant comment les Intérêts se propagent dans le réseau sans-fil. En particulier, pour les stratégies I&Aet MPR, une campagne de simulation est menée pour mieux

, Ceci est dû à la localité de la stratégie d'acheminement, qui ne demande que de connaître des informations locales (i.e., la position des voisins) plutôt que des informations globales (un prochain saut pour chaque nom supporté sur le réseau IoT). De plus, l'acheminement géographique offre des avantages pour les réseaux très dynamique grâce à son trafic de contrôle local moins couteux que l'inondation -même optimisée avec MPR -du réseau de capteurs sans-fil, Le modèle permet de montrer deux résultats principaux. D'abord, en termes de faisabilité, l'acheminement géographique offre des avantages en termes d'utilisation mémoire pour les topologies denses et/ou larges

, Contrôle d'admission pour applications à temps de réponse contraint

, Dans un second temps, le problème du contrôle d'admission dans des plateformes de type Fog est traité. En effet, pour être utiles, les données produites par les capteurs doivent pouvoir être traitées en temps limité, vol.17

, proche dans l'endroit où les données IoT sont produites et consommées afin de réduire la latence. Néanmoins, et en comparaison avec le Cloud, le Fog manque d'élasticité. Il est donc important de contrôler la charge du Fog pour garantir le temps de réponse de l'application. En particulier, cette section contient une proposition de contrôle d'admission de requêtes dans un noeud de type Fog

, Afin de répartir les requêtes entre Fog et Cloud, une stratégie de contrôle d'admission est déployée devant le Fog. Pour chaque requête, la stratégie décide ou bien d'accepter la requête dans le Fog, ou bien de la rediriger vers le Cloud. La problématique à résoudre est donc la suivante : comment trouver une stratégie qui minimise les coûts de Cloud tout en gardant le temps de réponse moyen sous une limite donnée ? Pour répondre à cette problématique, un modèle mathématique est proposé, B.3.1 Contrôle d'admission dans le Fog et modélisation La situation considérée est la suivante : un utilisateur envoie une requête pour une certaine donnée IoT. Pour produire cette donnée, il est nécessaire de récupérer une ou plusieurs mesures brutes de capteurs et d'effectuer des calculs sur ces mesures brutes, vol.34

, Pour faire cette sélection, il faut donc être capable de (i) identifier la donnée cible pour chaque requête et (ii) d'en extraire des prédictions de popularités. La solution présentée dans cette thèse, appelée le LRU-AC, remplit ces deux conditions, avec l'avantage supplémentaire d'avoir une implémentation performante en termes de débit, Il y a trois chemins possibles pour une requête dans le système Fog-Cloud : le Cloud, le cache du Fog, ou le système de calcul du Fog

L. Le, Hybride-ICN) [154]. hICN est une architecture ICN construite dans IPv6/TCP, c'est à dire que les champs des en-têtes IP et TCP sont utilisé pour fournir une sémantique ICN. En particulier, les données sont nommées en utilisant (pour un Intérêt) l'adresse IPv6 de destination sous la forme d'une location (préfixe de 64 bits) et d'un identifiant (suffixe de 64 bits). Cette architecture est particulièrement intéressante pour le cas d'usage du contrôle d'admission en FPGA, car elle permet d'accéder à une sémantique applicative dans l'en-tête réseau grâce à des champs qui ont une position et une taille constante, Identification des données cibles Pour identifier les données cibles

L. Le-lru-ac-utilise-un-filtre and I. E. , En effet, la présence (resp. absence) d'un identifiant dans un tel cache indique qu'il s'agit probablement d'une donnée populaire à accepter dans le Fog, Prédiction de popularité Pour prédire la popularité des requêtes

, En particulier, le comportement d'un tel cache peut être prédit en fonction de la distribution de popularité des requêtes grâce à l'approximation de Che [165], ce qui permet d'intégrer cette stratégie dans le modèle de file d'attente présenté plus haut