Sensor web systems, cyber-physical systems, and the so-called Internet of Things are concepts that share a set of common characteristics. The nature of such systems is highly dynamic and very heterogeneous and issues such as interoperability, energy consumption, or resource management must be properly managed to ensure the operation of the applications within the required quality of service level. In this context, base technologies such as component based software engineering or Service Oriented Architecture can play a central role. Model driven development and middleware technologies also aid in the design, development, and operation of such systems. This paper presents a middleware solution that provides runtime support for the complete lifecycle management of a system consisting of several concurrent applications running over a set of distributed infrastructure nodes. The middleware builds up on top of a general purpose component model and is driven by a quality of service aware self-configuration algorithm that provides stateful reconfiguration capabilities in face of both internal (application triggered) and external (application unaware) reconfiguration events. The platform has been deployed over an automated warehouse supervision system that serves as a case study.

With cloud computing gaining momentum in industrial environments, the next step seems to move the computing infrastructure closer to the devices that collect the data at plant level. Fog computing is a paradigm that takes advantage of the fast response times of working relatively close to the plant, and the storage, processing and availability features of the cloud. Fog computing can be used to improve the controllability of automation processes by introducing a higher-level control loop: Fog-in-the-loop (FIL). FIL allows capturing data from the plant, processing it to extract information and feedback actions to the plant based on the processing results. Therefore, FIL applications are context-aware applications that require the deployment of distributed components and dynamic reconfiguration. In this context, we are involved in a project that aims at integrating a model-based, multipurpose Multi-Agent System (MAS) platform with a containerized application orchestrator to meet the requirements of Fog-in-the-loop applications. Specifically, this paper describes a custom scheduler for Kubernetes orchestrator that distributes the scheduling task among the processing nodes by means of the MAS. This new scheduling approach proved to be faster than the centralized scheduling approach used by the default scheduler of K8s.

The continuous changes of the market and customer demands have forced modern automation systems to provide stricter Quality of service (QoS) requirements. This work is centered in automation production system flexibility, understood as the ability to shift from one controller configuration to a different one, in the most quick and cost-effective way, without disrupting its normal operation. In the manufacturing field, this allows to deal with non-functional requirements such as assuring control system availability or workload balancing, even in the case of failure of a machine, components, network or controllers. Concretely, this work focuses on flexible applications at production level, using Programmable Logic Controllers (PLCs) as primary controllers. The reconfiguration of the control system is not always possible as it depends on the process state. Thus, an analysis of the system state is necessary to make a decision. In this sense, architectures based on industrial Multi Agent Systems (MAS) have been used to provide this support at runtime. Additionally, the introduction of these mechanisms makes the design and the implementation of the control system more complex. This work aims at supporting the design and development of such flexible automation production systems, through the proposed model-based framework. The framework consists of a set of tools that, based on models, automate the generation of control code extensions that add flexibility to the automation production system, according to industry 4.0 paradigm.

In developed countries, public health systems are under pressure due to the increasing percentage of population over 65. In this context, homecare based on ambient intelligence technology seems to be a suitable solution to allow elderly people to continue to enjoy the comforts of home and help optimize medical resources. Thus, current technological developments make it possible to build complex homecare applications that demand, among others, flexibility mechanisms for being able to evolve as context does (adaptability), as well as avoiding service disruptions in the case of node failure (availability). The solution proposed in this paper copes with these flexibility requirements through the whole life-cycle of the target applications: from design phase to runtime. The proposed domain modeling approach allows medical staff to design customized applications, taking into account the adaptability needs. It also guides software developers during system implementation. The application execution is managed by a multi-agent based middleware, making it possible to meet adaptation requirements, assuring at the same time the availability of the system even for stateful applications.

The continuous changes on the market and customer demands have forced modern automation systems to provide more strict Quality of Service (QoS) requirements such as load balance or full availability of the control system, among other characteristics. In order to cope with these demanding requirements, modern automation systems have started to incorporate novel design approaches which include reconfiguration mechanisms. Following this trend, this paper presents the use of MDE techniques and technologies, to help in the definition of the control system of reconfigurable automation plants. Directly from the model-based design, a model-based tool is derived that provides support for the generation of the elements that compose the reconfigurable control system.

The sustainable development goals (SDGs) reflect the relevance that sustainability is gaining in our societies. Including sustainability-related topics in university curricula requires the revision of the contents, teaching/learning strategies and assessment techniques. Although engineering degrees are starting to introduce them, it may become complex to design significant educational experiences. This partly comes from the fact that sustainability is a highly multidisciplinary issue but, currently, the knowledge is compartmentalised into subjects. In this challenging scenario, concrete activities are required for students to better internalise sustainability issues. This work aims to present a methodology that guides academic staff to materialise the design of sustainability-related multidisciplinary activities. Since the designers of new activities may benefit from knowledge of similar experiences, this article describes one implementation throughout eight subjects within the Bachelor’s degree on Industrial Electronics and Automation Engineering at UPV/EHU. The analysis and optimisation of the thermal comfort and energy consumption in the Faculty building became the common thread to design an educational itinerary that covers several subjects along all academic years of the degree, making use of active methodologies. The problem is analysed for every subject from different perspectives. Two questionnaires, carried out before and after the activities, were used for analysing the perception of the students after the activities. Results proved that the multidisciplinary project raised awareness about the SDGs and allowed students to visualise how to apply the acquired skills in problems close to their experience. As a result, more students considered sustainability as a possible future professional activity.