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The world population is continuously growing and reached a significant evolution of the society, where the number of people living in cities surpassed the number of people in rural areas. This puts national and local governments under pressure because the limited resources, such as water, electricity, and transports, must thus be optimized to cover the needs of the citizens. Therefore, different tools, from sensors to processes, service, and artificial intelligence, are used to coordinate the usage of infrastructures and assets of the cities to build the so called smart cities. Different definitions and theoretical models of smart cities are given in literature. However, smart city can usually be modelled by a layered architecture, where communication and networking layer plays a central role. In fact, smart city applications lay on collecting field data from different infrastructures and assets, processing these data, taking some intelligent control actions, and sharing information in a secure way. Thus, a two way reliable communications layer is the basis of smart cities. This chapter introduces the basic concepts of this field and focuses on the role of communication technologies in smart cities. Potential technologies for smart cities are discussed, especially the recent wireless technologies adapted to smart city requirements.

The deployment of 4G/LTE (Long Term Evolution) mobile network has solved the major challenge of high capacities, to build real broadband mobile Internet. This was possible mainly through very strong physical layer and flexible network architecture. However, the bandwidth hungry services have been developed in unprecedented way, such as virtual reality (VR), augmented reality (AR), etc. Furthermore, mobile networks are facing other new services with extremely demand of higher reliability and almost zero-latency performance, like vehicle communications or Internet-of-Vehicles (IoV). Using new radio interface based on massive MIMO, 5G has overcame some of these challenges. In addition, the adoption of software defend networks (SDN) and network function virtualization (NFV) has added a higher degree of flexibility allowing the operators to support very demanding services from different vertical markets. However, network operators are forced to consider a higher level of intelligence in their networks, in order to deeply and accurately learn the operating environment and users behaviors and needs. It is also important to forecast their evolution to build a pro-actively and efficiently (self-) updatable network. In this chapter, we describe the role of artificial intelligence and machine learning in 5G and beyond, to build cost-effective and adaptable performing next generation mobile network. Some practical use cases of AI/ML in network life cycle are discussed.

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In recent years, Flipped Classroom started to be used as an effective way of teaching Engineering among various strategies in higher education. However, enabling and using the flipped learning is a complicated task, not a straightforward goal that can be simply achieved through a combination of face-to-face and online activities. It requires a more sophisticated understanding of effective teaching methods to manage the shift from the traditional to the flipped learning and the optimum adaptation of technology as part of this change. Given this challenge, this research work provides a personalized model of the flipped classroom and investigates through a case study in an Engineering School how our approach can be used to improve teaching of Information and Communication Technology (ICT) Engineering. It assesses by using empirically data related to the interaction of the various actors at different levels of abstraction, particularly from a gender perspective, the relevance and the impact of the flipped classroom on student learning and achievement in ICT Engineering Education.

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Dans cet article, nous présentons la mise en oeuvre, l’expérimentation et l’évaluation d’une approche pour la recommandation des chemins d’apprentissage dans un cours en ligne. Le processus de recommandation est inspiré de l’intelligence en essaim et plus particulièrement de l’optimisation par colonies de fourmis (OCF) (ant colony optimization [ACO]). Dans ce contexte, nous avons considéré une différenciation des chemins d’apprentissage en fonction de l’activité explorée pour l’apprentissage d’un cours.Dans l’objectif de recommander des chemins d’apprentissage considérés optimaux et d’évaluer ainsi leur impact sur l’apprentissage d’un cours en ligne, l’approche proposée est basée à la fois sur la recommandation de chemins pertinents par l’enseignant et sur les résultats stockés au fur et à mesure par les apprenants sur les chemins empruntés. Notre approche a été validée expérimentalement et les résultats obtenus ont montré l’émergence d’un chemin d’apprentissage favorisant la réussite d’un nombre d’apprenants relativement considérable.In this article, we present the implementation, the experimentation and the evaluation of an approach for the recommendation of learning paths in an online course. The recommendation process is inspired by swarm intelligence and especially the ant colony optimization (ACO). In this context, we considered a differentiation of the learning paths based on the explored educational activity for learning a lesson.With the aim to recommend learning paths considered optimal and thus assess their impact on the learning of an online course, the proposed approach is based both on the recommendation of relevant paths by the teacher and the result stored progressively by learners on the paths taken. Our approach was validated experimentally and the results showed the emergence of a learning path promoting the success of a relatively considerable number of learners