As the complexity of Internet of Things (IoT) systems increases, a large variety of tools and technologies for IoT management are making their way into both research setups and the market. IoT management solutions must consider the resource restrictions of embedded devices, as well as their heterogeneity and network dynamics. With these in mind, the Internet Engineering Task Force developed several standards targeting the integration and interoperation of heterogeneous devices, such as the Representational State Transfer Configuration Protocol (RESTCONF) or the Constrained Application Protocol (CoAP) Management Interface. Concurrently, the Open Mobile Alliance developed the Lightweight Machine-to-Machine protocol, for IoT device management. This paper provides a comprehensive, up-to-date overview of IoT management technologies, frameworks and protocols. Also, it proposes a taxonomy for IoT devices management. In addition to presenting the various solutions, the paper provides comparative views, standardization timeline, and market analysis. The presented analysis ranges from traditional network management protocols, such as Simple Network Management Protocol, to the newest IoT management and configuration protocols, such as CoAP Management Interface and Lightweight Machine-to-Machine protocols. Moreover, this survey identifies remaining challenges and solutions offered by recent management protocols, not covered by previous surveys.

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.

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 named5GOpenclasses, where we leveraged FIWARE middleware to manage all entities. This paper presents the architecture, technological platform, associated data structures, and end-user applications of5GOpenclasses. 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.

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 article, 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 use cases. 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.