Recently, mobile crowd sensing (MCS) is captivating growing attention because of their suitability for enormous range of new types of context-aware applications and services. This is attributed to the fact that modern smartphones are equipped with unprecedented sensing, computing, and communication capabilities that allow them to perform more complex tasks besides their inherent calling features. Despite a number of merits, MCS confronts new challenges due to network dynamics, the huge volume of data, sensing task coordination, and the user privacy problems. In this paper, a comprehensive review of MCS is presented. First, we highlight the distinguishing features and potential advantages of MCS compared to conventional sensor networks. Then, a taxonomy of MCS is devised based on sensing scale, level of user involvement and responsiveness, sampling rate, and underlying network infrastructure. Afterward, we categorize and classify prominent applications of MCS in environmental, infrastructure, social, and behavioral domains. The core architecture of MCS is also described. Finally, we describe the potential advantages, determine and reiterate the open research challenges of MCS and illustrate possible solutions.

Les systèmes d'énergie solaire et éolienne sont omniprésents, librement disponibles, respectueux de l'environnement et sont considérés comme des sources d'énergie prometteuses en raison de leur disponibilité et de leurs avantages topologiques pour les générations d’énergies locales. Les systèmes hybrides d'énergie solaire-éolienne, utilisant deux sources d'énergie renouvelables, permettent d'améliorer l'efficacité du système, la fiabilité de l'alimentation et la réduction des besoins en stockage d'énergie pour les applications autonomes. Les systèmes hybrides solaires-éoliens deviennent populaires dans les applications de production d'électricité dans les régions éloignées en raison des progrès réalisés dans les technologies d'énergie renouvelable et de la hausse substantielle des prix des produits pétroliers. Dans cette thèse, nous nous proposons d'étudier une variété d'outils méthodologiques pour la simulation, l'optimisation et le contrôle des systèmes d'énergies solaire-éolienne hybrides autonomes avec stockage de batterie. On constate que les efforts continus de recherche et de développement dans ce domaine sont encore nécessaires pour améliorer la performance de ces systèmes, établir des techniques pour prédire avec précision leur production et les intégrer de manière fiable à d'autres sources de production d'énergie renouvelable ou conventionnelle.

Actually, wireless sensor networks represent a substantial part in IoT. However, their limitation requires a special consideration in IoT applications. For their integration with the internet, it is necessary to adapt such networks using different middleware, with taking into account various challenges such as heterogeneity and interoperability. Previously Service Oriented Architecture (SOA) was the suitable design, but with a better practice, a new design called microservice becomes the leader due to its high performance and its suitability for IoT applications. In this paper, we first survey the most important middleware that have been proposed to handle WSN through IoT. Also, we discuss the most crucial microservices that handle different integration factors by making them supported by the proposed middleware. Our proposal is inspired from artificial neural network architecture to allow dynamic service interaction; it supports unlimited services with a regard to various device capabilities separately of the cloud technologies. Moreover, the evaluation of our design clearly shows that our middleware allows a lightweight WSN integration with IoT regarding to their limitations and requirements.

Due to the intrinsically geo-distributed subcontracting maintenance activity practice, the maintenance scheduling has for a long time been a major challenge in the industry. This research work presents a methodology to schedule the maintenance activities of geo-distributed assets. A multi-agent system based approach is proposed to enable the decision-making for the subcontractors in a distributed industrial environment under uncertainty. An auction based negotiation mechanism is designed to promote competition and cooperation among the different agents, and to obtain global good schedule.Compared to the Weighted Shortest Processing Time first–Heuristic–Earliest Due Date (WSPT-H-EDD) method, the experimental results show that the proposed approach is able to provide scheduling scheme with good performances in terms of Global Cost, Total Weighted Tardiness Cost and makespan.

Electronic Nose, as an artificial olfaction system, has potential applications in environmental monitoring because of its proven ability to recognize and discriminate between a variety of different gases and odors. In this paper, we used a chemical sensor array to develop an electronic nose to detect and identify seven different gases (H2, C2H2, CH4, CH3OCH3, CO, NO2, and NH3). These gas sensors are chosen because of its hierarchical/doped nanostructure characteristics, which give them a very high sensitivity and low response time; we improve the linearity response and temperature dependence using models based on artificial neural networks. We used in Electronic nose a pattern recognition based on artificial neural network, which discriminates qualitatively and quantitatively seven gases and has a fast response.