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The next generation of mobile networks, widely known as 5G, was designed to respond to the next 10 years’ communication challenges. 5G will be an essential structure on which all types of electronic communications will rely. Built on the emerging 5G technology, we developed a vertical solution for blended learning environments within a Portuguese national project scope. The project aims to propose an integrated demonstration of a set of products capable of being part and providing services in the future 5G networks framework. To this end, we adopted a bottom-up approach to design and develop a product named 5GOpenclasses, where we leveraged FIWARE middleware to manage all entities. This paper presents the architecture, technological platform, associated data structures, and end-user applications of 5GOpenclasses. We also present the design of innovative location-based service for blended learning environments. This paper is the first step of the proposed product towards its quantitative evaluation when running Over-the-Top on both 4G and 5G networks. Although successful unit tests were carried out in what concerns the functional outcome, the integration tests for quantitative results depend on the availability of other project components.

The Internet keeps changing at a rapid pace, driven mainly by the emerging concepts and applications that make it aware of the physical world and responsive to user context. The Internet of Things (IoT) concept is quickly giving way to more advanced and highly interactive environments that go well beyond the mere sensing of the physical world. Today, in addition to traditional electronic devices, IoT sensing/actuating includes both software and human-based entities. This paper provides an outlook on the future of sensing/actuating approaches on the Internet at large, which we see increasingly related to all kinds of socially interactive technologies. With these objectives in mind, we propose a taxonomy to deal with the heterogeneity of sensing/actuating approaches in IoT. We also analyse the state-of-the-art of Social Sensing. Finally, we identify open issues and associated research opportunities, the main ones being the integration of all sensing approaches, the combination of social sciences, engineering, and computing as enablers of context-aware, cognitive applications and, last but not least, the unified management of large sets of very heterogeneous sensors/actuators.

Is it possible to analyze student academic performance using Human-in-the-Loop Cyber-Physical Systems (HiLCPS) and offering personalized learning methodologies? Taking advantage of the Internet of Things (IoT) and mobile phone sensors, this article presents a system that can be used to adapt pedagogical methodologies and to improve academic performance. Thus, in this domain, the present work shows a system capable of analyzing student behavior and the correlation with their academic performance. Our system is composed of an IoT application named ISABELA and a set of open-source technologies provided by the FIWARE Project. The analysis of student performance was done through the collection of data, during 30 days, from a group of Ecuadorian university students at “Escuela Politécnica Nacional” in Quito, Ecuador. Data gathering was carried out during the first period of classes using the students’ smartphones. In this analysis, we found a significant correlation between the students’ lifestyle and their academic performance according to certain parameters, such as the time spent on the university campus, the students’ sociability, and physical activity, etc.

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While the traditional Internet of Things (IoT) relies on electronic sensors/actuators, today's IoT involves a variety of sensors, comprising not only physical, electronic-based devices, but also virtual and even human social sensors/actuators. In this context, how can we efficiently and effectively manage different types of IoT data sources? In this paper, we propose a solution to manage different types of sensors/actuators and analyze which management protocols are best in terms of performance. The proposal is based on open and broadly adopted technologies in IoT, with emphasis on the FIWARE middleware. We showed that the management of the heterogeneity of the sensing/actuating IoT devices is feasible, by presenting functionalities related to real usecases. We took advantage of the implemented prototype to compare the performances of Lightweight Machine-to-Machine (LwM2M) and Ultralight device management services in FIWARE. In addition to demonstrating the viability of the proposed approach, the obtained results point to mixed advantages/disadvantages of one protocol over the other.