Résumé: he Internet of Things is an ecosystem which enables objects and devices such as sensors or actuators to communicate and exchange information with each other without human intervention. One of the main challenges in the Internet of Things is the lack of semantic interoperability; devices cannot understand the meaning of raw data, due to the diversity and heterogeneity in data formats from different sources. In order to deal with semantic interoperability, the ontologies are the one way to integrate semantics to raw data; they describe an IoT system and represent the data in a standardized way. The IoT devices provide a great deal of IoT data, mainly used for specific IoT applications such as smart home, smart farming, smart cities or healthcare. Therefore, existing applications became isolated in vertical silos, each one of them use independently their own model (i.e. ontology), which makes this ontologies also limited to a specific domain. Our approach has the goal of breaking down these vertical silos and achieves a semantic interoperability across IoT domains in cross-domain applications. In this paper, we have proposed a development of a single cross-domain ontology named CDOnto, it is considered to be a generic across different IoT domains, which can be extended by domain-specific ontologies. The proposed model follows a contextual approach to organize and distinguish the combined domains (i.e. contexts) representations. In addition, the ontology allows reasoning across overlapping domains and infers a complementary and new knowledge required in cross-domain applications.

Résumé: Due to the rapid growth of the Internet of Things (IoT), researchers have demonstrated various IoT solutions, which are used to interconnect a wide range of IoT devices through the Internet. However, IoT stumbled into vertical silos; the available solutions provide specific IoT infrastructure, devices, protocols, data formats and models. This diversity and heterogeneity lead to interoperability issues. Heterogeneity happens at all IoT layers, especially at the application layer; devices often adopt mutually incompatible application-layer communication protocols to connect devices to IoT services. Furthermore, in order to integrate semantics to raw data, each system uses its one domain-specific ontology to make data more understandable and interpretable by adding semantic annotations. Working in isolation reduces the interoperability among IoT devices and systems, things across domains need to internetwork and collaborate to provide high level IoT services. Therefore, to alleviate the problem of both communication protocol interoperability and semantic interoperability across vertical silos of systems at the application layer, this paper proposes a semantic gateway (SGIoT) that acts as a bridge between heterogeneous sink nodes at the physical level and IoT services. SGIoT enables interconnectivity between communication protocols such as CoAP and MQTT regardless of their communication model, meanwhile it enables semantics integration throu gh cross-domain ontology (CDOnto) for semantic annotation, in order to provide interpretation of messages among IoT applications across domains. Our approach focuses on modularity and extensibility.

Résumé: The Internet of Things (IoT) and Software Defined Network (SDN) are two emerging technologies. The IoT aims to connect devices over the Internet. Networks on which these IoT devices operate will continue to be heterogeneous and the network becomes more complex. SDN paradigm provides efficient network management by decoupling the control plane and the data plane, it allows facing network heterogeneity and interoperability (networks-to-network communications) through a programmable manner, this paradigm allows the communication between various objects independently of their respective technologies and thus allows dealing with the problem of heterogeneity. The main objective of this chapter is to present an overview of SDN-based pertinent solutions that consider network level interoperability and heterogeneity in the IoT.

Résumé: Feature selection is a preprocessing step that plays an important role in Data mining. It allows searching a reduced size of features' subset from a large set, by eliminating redundant and irrelevant features. These are often used to perform the supervised classification task, in order to maintain or improve classifier performance. The search for a features' subset is an NP-difficult optimization problem, which can be solved by metaheuristics; we are interested by the metaheuristics based on swarm intelligence for feature selection problem. In this paper, we propose a "Binary Clonal Quantum Particle Swarm Optimization" algorithm, denoted BC-QPSO for selecting a subset of relevant features. This algorithm is developed from hybridization between an "optimization of the binary quantum particle swarm (BQPSO)" metaheuristic and an artificial immune system using its clonal selection algorithm (CSA). The experiments are carried out using UCI databases (University of California, Irvine). Besides, the experimental results of our approach are compared with those obtained by two approaches proposed in the literature: "Binary Particle Swarm Optimization (BPSO)" and "Binary Quantum Particle Swarm Optimization (BQPSO)", the results obtained are competitive and show the efficiency of our algorithm BC-QPSO.