Cette thèse présente MELQART, un système d'exécution de mashups avec disponibilité de données. Un mashup est une application web qui combine des données provenant de fournisseurs hétérogènes (web services). Ces données sont agrégées pour former un résultat homogène affiché dans des composants appelés mashlets. Les travaux dans le domaine des mashups, se sont principalement intéressés au fonctionnement des mashups, aux différents outils de construction et à leur utilisation et interaction avec les utilisateurs. Dans cette thèse, nous nous intéressons à la gestion de données dans les mashups et plus particulièrement à la disponibilité et la fraîcheur de ces données. L'amélioration de la disponibilité tient compte du caractère dynamique des données des mashups. Elle garantit (1) l'accès aux données même si le fournisseur est indisponible, (2) la fraicheur de ces données et (3) un partage de données entre les mashups afin d'augmenter la disponibilité de données. Pour cela nous avons défini un modèle de description de mashups permettant de spécifier les caractéristiques de disponibilité des données. Le principe d'exécution de mashups est défini selon ce modèle en proposant d'améliorer la disponibilité et la fraicheur des données du mashup par des fonctionnalités orthogonales à son processus d'exécution. Le système MELQART implante ce principe et permet de valider notre approche à travers l'exécution de plusieurs instances de mashups dans des conditions aléatoires de rupture de communication avec les fournisseurs de données. / This thesis presents MELQART: a mashup execution system that ensures data availability. A mashup is a Web application that application that combines data from heterogeneous provides (Web services). Data are aggregated for building a homogenous result visualized by components named mashlets. Previous works have mainly focused, on the definition of mashups and associated tools and on their use and interaction with users. In this thesis, we focus on mashups data management, and more specifically on fresh mashups data availability. Improving the data availability take into account the dynamic aspect of mashups data. It ensures (1) the access to the required data even if the provider is unavailable, (2) the freshness of these data and (3) the data sharing between mashups in order to avoid the multiple retrieval of the same data. For this purpose, we have defined a mashup description formal model, which allows the specification of data availability features. The mashups execution schema is defined according to this model with functionalities that improve availability and freshness of mashed-up data. These functionalities are orthogonal to the mashup execution process. The MELQART system implements our contribution and validates it by executing mashups instances with unpredictable situations of broken communications with data providers.

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<p>It is predicted that there would be 41.6 billion IoT devices by 2025, which has kindled new interests on the timing coordination between sensors and controllers, i.e., how to use the waiting time to improve the performance. Sun et al. showed that a <i>controller</i> can strictly improve the data freshness, the so-called Age-of-Information (AoI), via careful scheduling designs. The optimal waiting policy for the <i>sensor</i> side was later characterized in the context of remote estimation. The first part of this work develops the jointly optimal sensor/controller waiting policy. It generalizes the above two important results in that not only do we consider joint sensor/controller designs, but we also assume random delay in both the forward and feedback directions. </p> <p> </p> <p>The second part of the work revisits and significantly strengthens the seminal results of Sun et al on the following fronts: (i) When designing the optimal offline schemes with full knowledge of the delay distributions, a new <i>fixed-point-based</i> method is proposed with <i>quadratic convergence rate</i>; (ii) When the distributional knowledge is unavailable, two new low-complexity online algorithms are proposed, which provably attain the optimal average AoI penalty; and (iii) the online schemes also admit a modular architecture, which allows the designer to <i>upgrade</i> certain components to handle additional practical challenges. Two such upgrades are proposed: (iii.1) the AoI penalty function incurred at the destination is unknown to the source node and must also be estimated on the fly, and (iii.2) the unknown delay distribution is Markovian instead of i.i.d. </p> <p> </p> <p>With the exponential growth of interconnected IoT devices and the increasing risk of excessive resource consumption in mind, the third part of this work derives an optimal joint cost-and-AoI minimization solution for multiple coexisting source-destination (S-D) pairs. The results admit a new <i>AoI-market-price</i>-based interpretation and are applicable to the setting of (i) general heterogeneous AoI penalty functions and Markov delay distributions for each S-D pair, and (ii) a general network cost function of aggregate throughput of all S-D pairs. </p> <p> </p> <p>In each part of this work, extensive simulation is used to demonstrate the superior performance of the proposed schemes. The discussion on analytical as well as numerical results sheds some light on designing practical network utility maximization protocols.</p>

Telemetry system, Optimisation, Sensoric networks, Smart Grid, Internet of Things, Sensors, Information security, Cryptography, Cryptography algorithms, Cryptosystem, Confidentiality, Integrity, Authentication, Data freshness, Non-Repudiation.