Abstract
This article discusses how a business model based on traditional maintenance can evolve to generate servitization strategies, with the help of remote maintenance support. The application of cyber-physical systems and cloud technologies play a key role for such maintenance purposes. In fact, the utilization of large quantities of data collected on machines and their processing by means of advanced techniques such as machine learning enable novel techniques for condition-based maintenance. New sensor solutions that could be used in maintenance and interaction with cyber-physical systems are also presented. Here, data models are an important part of these techniques because of the huge amounts of data that are produced and should be processed. These data models have been used in a real case, supported by the Machinery Information Management Open System Alliance Open System Architecture for Condition-Based Maintenance standard architecture, for streamlining the modeling of collected data. In this context, an industrial use case is described, to enlighten the application of the presented concepts in a working pilot. Finally, current and future directions for application of cyber-physical systems and cloud technologies to maintenance are discussed.

Abstract
Energy management in buildings can provide massive benefits in financial and energy saving terms. It is possible to optimize energy usage with smart grid techniques, where the benefits are enhanced when the energy consumer can trade the energy on energy markets, since it forces energy providers to compete with each other on the energy price. However, two hurdles oppose this approach: the devices providing control over appliances do not interoperate with each other; and energy markets limit trading activities to large quantities of energy, thus impeding access for small consumers. This work considers using the FlexOffer (FO) concept to allow the consumer to express its energy needs, and FO-related mechanisms to aggregate energy requests into quantities relevant for energy markets. Moreover, the presented system, named FlexHousing, is based on the Arrowhead Framework - a framework that simplifies design and implementation of distributed applications by means of normalizing communication via services - and exploits its Service Oriented mechanisms to provide device interoperability. The implemented FlexHousing system uses multi-level FO aggregation to empower either the final user, for example the owner of an apartment, to manage its own energy by defining their flexibilities, or to offload this responsibility to an energy manager who takes care of all the apartments in a building or set of buildings.

Abstract
This paper discusses the integration of emergent ICTs, such as the Internet of Things (IoT), the Arrowhead Framework, and the best practices from the area of condition monitoring and maintenance. These technologies are applied, for instance, for roller element bearing fault diagnostics and analysis by simulating faults. The authors first undertook the leading industry standards for condition-based maintenance (CBM), i.e., open system architecture-condition-based maintenance (OSA-CBM) and Machinery Information Management Open System Alliance (MIMOSA), which has been working towards standardizing the integration and interchangeability between systems. In addition, this paper highlights the predictive health monitoring methods that are needed for an effective CBM approach. The monitoring of industrial machines is discussed as well as the necessary details are provided regarding a demonstrator built on a metal sheet bending machine of the Greenbender family. Lastly, the authors discuss the benefits of the integration of the developed prototypes into a service-oriented platform, namely the Arrowhead Framework, which can be instrumental for the remotization of maintenance activities, such as the analysis of various equipment that are geographically distributed, to push forward the grand vision of the servitization of predictive health monitoring methods for large-scale interoperability.

Abstract
Collecting complex information on the status of machinery is the enabler for advanced maintenance activities, and one of the main players in this process is the sensor. This paper describes modern maintenance strategies that lead to Condition-Based Maintenance. This paper discusses the sensors that can be used to support maintenance, as of different categories, spanning from common off-the-shelf sensors, to specialized sensors monitoring very specific characteristics, and to virtual sensors. This paper also presents four different real-world examples of project pilots that make use of the described sensors and draws a comparison between them. In particular, each scenario has unique characteristics requiring different families of sensors, but on the other hand provides similar characteristics on other aspects.

Abstract
The remote maintenance of home appliances, like washing machines, air conditioning, and heating system is a complex problem, but with the help of the ongoing developments on Internet of Things, Data Analysis and Artificial Intelligence, the problem can now be tackled with success. This paper mostly focus in presenting the architecture developed within the aim of the SMART-PDM project for the acquisition of data on the operation of home appliances and then it also shows some preliminary results for washing machines, which give some hints on how to fine tune the system to achieve predictive maintenance and condition monitoring.

Abstract
Renewable Energy Communities (RECs) are emerging as an effective concept and model to empower the active participation of citizens in the energy transition, not only as energy consumers but also as promoters of environmentally friendly energy generation solutions, particularly through the use of photovoltaic panels. This paper aims to contribute to the management and optimization of individual and community Distributed Energy Resources (DER). The solution follows a price and source-based REC management program, in which consumers' day-ahead flexible loads (Flex Offers) are shifted according to electricity generation availability, prices, and personal preferences, to balance the grid and incentivize user participation. The heuristic approach used in the proposed algorithms allows for the optimization of energy resources in a distributed edge-and-fog approach with a low computational overhead. The simulations performed using real-world energy consumption and flexibility data of a REC with 50 dwellings show an average cost reduction, taking into consideration all the seasons of the year, of 6.5%, with a peak of 12.2% reduction in the summer, and an average increase of 32.6% in individual self-consumption. In addition, the case study demonstrates promising results regarding grid load balancing and the introduction of intra-community energy trading.