The reliability and safety of industrial machines depends on their timely maintenance. The integration of Cyber Physical Systems within the maintenance process enables both continuous machine monitoring and the application of advanced techniques for predictive and proactive machine maintenance. The building blocks for this revolution - embedded sensors, efficient preprocessing capabilities, ubiquitous connection to the internet, cloud-based analysis of the data, prediction algorithms, and advanced visualization methods - are already in place, but several hurdles have to be overcome to enable their application in real scenarios, namely: the integration with existing machines and existing maintenance processes. Current research and development efforts are building pilots and prototypes to demonstrate the feasibility and the merits of advanced maintenance techniques, and this paper describes a system for the industrial maintenance of sheet metal working machinery and its evolution towards a full proactive maintenance system.

The lack of interoperability among systems, applications, and services has been hindering collaboration between organisations raising the need for integration and cooperation between multiple standard business objects and application protocols. ISO 10303-STEP has been acknowledged by most of the industrial companies as the most important family of standards for the integration and exchange of product data under the manufacturing domain. However, STEP standards use technologies unfamiliar to most application developers. This paper presents a platform for the harmonisation of technologies used in the representation and implementation of conceptual models, showing how different standard models and technologies can be unified for industrial benefits, enabling organisations to take the most from all of them. This work results from the research developed and validated in the scope of international research projects involving partners from USA, Europe, Canada, and Australia, under the scope of the IMS SMART-fm, ATHENA IP, and INTEROP NoE European research projects.

Objective: Long term behavioural disturbances and interventions in healthy habits (mainly eating and physical activity) are the primary cause of childhood obesity. Current approaches for obesity prevention based on health information extraction lack the integration of multi-modal datasets and the provision of a dedicated Decision Support System (DSS) for health behaviour assessment and coaching of children. Methods: Continuous co-creation process has been applied in the frame of the Design Thinking Methodology, involving children, educators and healthcare professional in the whole process. Such considerations were used to derive the user needs and the technical requirements needed for the conception of the Internet of Things (IoT) platform based on microservices. Results: To promote the adoption of healthy habits and the prevention of the obesity onset for children (9-12 years old), the proposed solution empowers children -including families and educators- in taking control of their health by collecting and following-up real-time information about nutrition, physical activity data coming from IoT devices, and interconnecting healthcare professionals to provide a personalised coaching solution. The validation has two phases involving +400 children (control/intervention group), on four schools in three countries: Spain, Greece and Brazil. The prevalence of obesity decreased in 75.5% from baseline levels in the intervention group. The proposed solution created a positive impression and satisfaction from the technology acceptance perspective. Conclusions: Main findings confirm that this ecosystem can assess behaviours of children, motivating and guiding them towards achieving personal goals.

In order to create distributed automation systems, it is required to ensure their interoperability; however, ensuring interoperability between heterogeneous systems (using different communication protocols, data formats, and semantics) is a challenging task. Among many interoperability challenges, this paper addresses issues concerning semantic and data interoperability, namely, it provides a contribution to support the semantic compatibility verification and the generation of translators for XML messages. Translators are generated based on XML-Schemas that are annotated with a reference ontology. We base our annotations on an extension of an existing declarative annotation method. In particular our extension explicitly addresses ambiguities of annotations, schema instance mismatches and coverage mismatches that frequently occur on IoT message schemas. We have evaluated our approach based on a set of interaction scenarios from the domain of the Arrowhead Project. A tool prototype that supports the semantic compatibility verification and the generation of XML translators is available at http://gres.uninova.pt/tag/.

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.

Presently, middleware technologies abound for the Internet-of-Things (IoT), directed at hiding the complexity of underlying technologies and easing the use and management of IoT resources. The middleware solutions of today are capable technologies, which provide much advanced services and that are built using superior architectural models, they however fail short in some important aspects: existing middleware do not properly activate the link between diverse applications with much different monitoring purposes and many disparate sensing networks that are of heterogeneous nature and geographically dispersed. Then, current middleware are unfit to provide some system-wide global arrangement (intelligence, routing, data delivery) emerging from the behaviors of the constituent nodes, rather than from the coordination of single elements, i.e. self-organization. This paper presents the SIMPLE self-organized and intelligent middleware platform. SIMPLE middleware innovates from current state-of-research exactly by exhibiting self-organization properties, a focus on data-dissemination using multi-level subscriptions processing and a tiered networking approach able to cope with many disparate, widespread and heterogeneous sensing networks (e.g. WSN). In this way, the SIMLE middleware is provided as robust zero-configuration technology, with no central dependable system, immune to failures, and able to efficiently deliver the right data at the right time, to needing applications.