Heating appliances consume approximately $48\%$ of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment’s malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.

Network slicing emerges as a key technology in next generation networks, boosted by the integration of software‐defined networking and network functions virtualization. However, while allowing resource sharing among multiple tenants, such networks must also ensure the security requirements needed for the scenarios they are employed. This letter presents the leading security challenges on the use of network slices at the packet core, the solutions that academy and industry are proposing to address them, pointing out some directions that should be considered.

Estimating the knee/elbow point in error curves is a challenging task. However, most of the time these points represent ideal compromises or ideal parameters for several tasks, methods and algorithms. Our focus is determining the ideal number of clusters autonomously. In this paper, we formalize the notion of knee/elbow point, discuss known methods to determine it and propose our own method. Contrary to most methods, ours is resilient to long tails in the error curve. This behaviour is especially important when considering autonomous methods. The proposed method outperformed the competition on five datasets from UCI Machine Learning Repository.

Delegation of processing tasks to the network has moved from cloud-based schemes to edge computing solutions where nearby servers process requests in a timely manner. Virtualisation technologies have recently given data cloud and network providers the required flexibility to offer such on-demand resources. However, the maintenance of close computing resources presents a challenge when the served devices are on the move. In this case, if processing continuity is desired, a transference of processing resources and task state should be committed to maintain the service to end devices. The solution here presented, MIGRATE, proposes the concept of virtual mobile devices (vMDs) implemented as Virtual Functions (VxF) and acting as virtual representatives of physical processing devices. vMDs are instantiated at the edge of the access network, following a Multi-Access Edge Computing (MEC) approach, and move across different virtualisation domains. MIGRATE provides seamless and efficient transference of these software entities to follow the real location of mobile devices and continue supporting their physical counterparts. Software Defined Networks and Management and Operation functions are exploited to “migrate” vMDs to new virtualisation domains by forwarding data flows to the former domain until the new one is prepared, while a distributed data base avoids the transference of data. The solution has been deployed in a reference vehicular scenario at the Institute of Telecommunications Aveiro premises within the 5GINFIRE European project. In particular, the system has been evaluated under different virtualisation domains to study the operation of the migration approach in a vehicular monitoring scenario. The results validate the system from the application viewpoint with a Web monitoring tool, and the migration of the digital twin provided as VxF is analysed attending to the modification of data flows, indicating a seamless transition between virtualisation domains in a timely manner.