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[EN] Object recognition, which can be used in processes such as reconstruction of the environment map or the intelligent navigation of vehicles, is a necessary task in smart city environments. In this paper, we propose an architecture that integrates heterogeneously distributed information to recognize objects in intelligent environments. The architecture is based on the IoT/Industry 4.0 model to interconnect the devices, which are called smart resources. Smart resources can process local sensor data and offer information to other devices as a service. These other devices can be located in the same operating range (the edge), in the same intranet (the fog), or on the Internet (the cloud). Smart resources must have an intelligent layer in order to be able to process the information. A system with two smart resources equipped with different image sensors is implemented to validate the architecture. Our experiments show that the integration of information increases the certainty in the recognition of objects by 2-4%. Consequently, in intelligent environments, it seems appropriate to provide the devices with not only intelligence, but also capabilities to collaborate closely with other devices., This research was funded by the Spanish Science and Innovation Ministry grant number MICINN: CICYT project PRECON-I4: "Predictable and dependable computer systems for Industry 4.0" TIN2017-86520-C3-1-R.

[ES] En este artículo se describe una estrategia de control distribuida, utilizando elementos empotrados, mediante un PLGGOHZDUH de control denominado núcleo de control, en el que se implementan controladores digitales de altas prestaciones, diseñados de forma modular, en sistemas con capacidad de cómputo limitada. Se presenta una metodología tanto para la obtención de una métrica que permita la comparación de los distintos modos de funcionamiento ante pérdidas de datos, como para la elección de los parámetros de los controladores a implementar en cada nodo del sistema distribuido de control. Se presentan varios modos de funcionamiento que permite adaptar el sistema desarrollado a distintas situaciones. El trabajo se completa con la implementación de la simulación del sistema distribuido y su prueba sobre un proceso real., [EN] In this paper a distributed control strategy described using embedded items, using a control middleware called control kernel, in which high performance digital controllers designed in a modular way, on systems with limited computational capabilities are implemented. A methodology is presented for both obtaining a metric that allows comparison of different modes of operation for any loss of data, and for the choice of parameters of the controllers implemented in each node of the distributed control system. Multi-mode which allows adapting the system developed at different situations arise. The work was completed with the implementation of distributed system simulation and test of a real process., Este trabajo ha sido parcialmente financiado por el proyecto CICYT COBAMI con referencia DPI 2011-28507-C02-01/02, del Ministerio de Educación y Ciencia.

[EN] Robot behavior definition aims to classify and specify the robot tasks execution. Behavior architecture design is crucial for proper robot operation performance. According to this, this work aims to establish a robot behavior architecture based on distributed intelligent services. Therefore, behavior definition is set in a high-level delegating the task execution to distributed services provided by network abstractions characterized as Smart Resources. In order to provide a mechanism to measure the performance of this architecture, an evaluation mechanisms based on a service performance composition is introduced. In order to test this proposal it is designed a real use case implementing the proposed robot behavior architecture on a real navigation task., Work supported by the Spanish Science and Innovation Ministry MICINN: CICYT 866 project M2C2: Codiseño de sistemas de control con criticidad mixta basado en 867 misiones TIN2014-56158-C4-4-P and PAID (Polytechnic University of Valencia): 868 UPV-PAID-FPI-2013.

[EN] Mobile robots execute complexes tasks that involve the management of several embedded sensors and actuators. Therefore, in many cases, a robot is characterized as an intelligent distributed system formed with a central unit, which manages the on-board embedded devices and distributes the tasks execution. Embedded devices are also evolving to more complex systems. These systems are developed not only for executing simple tasks but also for offering some advanced mechanisms. Thus, complex data processing, adaptive execution, or fault-tolerance routines are some common system features. The Smart Resource topology has been developed in order to manage these embedded systems. This topology offers high-level routines that rely on a certain physical hardware execution. Therefore, Smart Resources are defined as distributed services providers, which operates within some context and quality requirements. Provided services can adapt its execution in order accomplish the set requirements and maximize the system performance. How to improve the versatility of the Smart Resources by making their services compatibles with the Robot Operating System (ROS) is addressed along this work. This solution integrates all the execution mechanisms provided by ROS with the service distribution, adaptive execution, and fault-tolerance routines offered by the Smart Resources. This integration is tested through a set of experiments using the Turtlebot robot platform and a simulated version of it. In both approaches ROS mechanisms are used to access the Smart Resource Services. Finally, obtained results are used to characterize the performance of this proposal., Work supported by the Spanish Science and Innovation Ministry MICINN: CICYT project M2C2: "Codiseno de sistemas de control con criticidad mixta basado en misiones" TIN2014-56158-C4-4-P and PAID (Polytechnic University of Valencia): UPV-PAID-FPI-2013.

The student-centered learning is being promoted worldwide in higher education, from 2005, in North-American universities [Principles of teaching. Carnegie Mellon University [Online]. Available www.cmu.edu/ teaching/principles/ teaching.html], in 2010 in the European Union, but also in Asian countries like Japan [J. M. Bachnik, Roadblocks on the Information Highway: The IT Revolution in Japanese Education, ser. Studies of Modern Japan. Lexington Books, 2003.]. This paradigm is characterized by applying innovative methods of teaching that involve students as active participants in their own learning. In this paper, it is proposed a method based on detecting the students’ learning gains by using low time consuming “Pretests” and “Posttests”, on a selection of learning activities. The overload of introducing the “Pretest-Posttest” is minimized by using information and communication technologies. The results of the “Pretests” and “Posttests”, provided online, allow the students to be aware of their learning progress. The analysis of these results helps teachers adapt their strategies when presenting the learning activities. The method has been applied on a first-year course on Computer Engineering, during the 2010–2011, 2011–2012 and 2012–2013 academic years. To verify that the method works, two student groups, one control and one experimental, were created. The method was applied in the experimental group. The statistical analysis of the obtained results by the experimental and control groups, shows that the method enhances the grades of the experimental group. Additionally, the support of Information and Communication Technologies minimizes the overload of using this method. 2015 Wiley Periodicals, Inc. Comput Appl Eng Educ 23:610–620, 2015; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21633, Contract grant sponsor: Spanish Government; Contract grant number: TIN 2012-32180

[EN] This paper introduces how mobile robots can perform navigation tasks by implementing a system based on the control kernel middleware (CKM), and how can take benefit of this. Smart resources are also included into the topology of the system, improving the processing and distribution of the computational load required by the system tasks. The CKM and the smart resources are both highly reconfigurable, even on execution time, and they also implement fault detection mechanisms and Quality of Service (QoS) policies. By combining of these capabilities, the system can be dynamically adapted to the requirements of its tasks. Furthermore, this solution is designed to be implemented by almost every type of robot. The distribution of load make this system suitable even for those configurations which are provided with a low computational power. All these benefits are improved by exploiting the smart resources capabilities, and the dynamic performance of the system., This work has been partially supported by the Spanish Ministry of Economy and Competitiveness under the CICYT project Mission Based Control (COBAMI): DPI2011-28507-C02-02.

[EN] This paper presents the architecture of a mobile-based distributed system which aims to minimize the social impact of abandoned or lost animals. To reach this purpose the distributed system has two main goals: to obtain the best pets quality information and to optimize the reporting process that allows their rescue. The distributed system is based on the client-server paradigm. By using smart mobile devices clients may provide message warnings of animals localized. In order to enter data such as photographs, audio and artificial images, this system uses different mobile device interfaces. Messages with the pet information are stored in a server database to be processed intelligently. Data processing consists mainly in matching localized animals with lost animals, assigning abandoned animals to shelters and generating notifications for animal shelters or authorities. This matching uses an efficient selection algorithm based on common meta-information to each image. This efficient selection facilitates selective notification deliveries avoiding redundancy. This article shows the architecture details, as well as the first achieved results with prototypes and real data., The study described in this paper is a part of the coordinated project COBAMI: Mission-based Hierarchical Control. Education and Science Department, Spanish Government. CICYT: MICINN: DPI2011-28507-C02-01/02 and the project "Real time distributed control systems" of the Support Program for Research and Development 2012 UPV (PAID-06-12)

This article presents the experimental work developed to test the viability and to measure the efficiency of the intelligent control distributed architectures. To do this, a simulated navigation scenario of Braitenberg vehicles has been developed. To control the vehicles, system uses a distributed control architecture that provides support to QoS and QoC parameters to optimize de system. The architecture uses a Publish-Subscribe model, based on Data Distribution Service to send the control messages. Due to the nature of the Publish-Subscribe model, the architecture is suitable to implement event-based control systems. The architecture has been called FSACtrl. To test the efficiency, the architecture provides a set of Quality of Service parameters. In the experiment described in this paper, the performance is used as a reference. The measuring of the quality of the navigation is done through the Integrated Time Average Error as Quality of Control parameter. Tested scenarios are: an environment without quality parameter managing, an environment with a relevant message filtering and an environment with a predictive filtering determined by the type of scenario used. Results obtained show that some of the processing performed in the control nodes can be moved to the middleware to optimize the robot navigation.

In a Multi-Agent system, middleware is one of the components used to isolate control and communications. The use of standards in the implementation of an intelligent distributed system is always advantageous. This paper presents a middleware that provides support to a multi-agent system. Middleware is based on the standard Data Distribution Services (DDS), proposed by Object Management Group (OGM). Middleware organizes information by tree based ontology and provides a set of quality of service policies that agents can use to increase efficiency. DDS provides a set of quality of service policy. Joining quality of service policy and the ontology allows getting many advantages, among others the possibility of to conceal some details of the communications system to agents, the correct location of the agents in the distributed system, or the monitoring agents in terms of quality of service. For modeling the middleware architecture it has used UML class diagrams. As an example it has presented the implementation of a mobile robot navigation system through agents that model behaviors., The MAS architecture described in this article is a part of the coordinated project SIDIRELI: Distributed Systems with Limited Resources. Control Kernel and Coordination. Education and Science Department, Spanish Government. CICYT: MICINN: DPI2008-06737-C02-01/02.

Quality of service policies in communications is one of the current trends in distributed systems based on middleware technology. To implement the QoS policies it is necessary to define some common parameters. The aim of the QoS policies is to optimize the user defined QoS parameters. This article describes how to obtain the common QoS parameters using message queues for the communications and control components of communication. The paper introduces the Queue-based Quality of Service Cycle concept for each middleware component. The QoS parameters are obtained directly from the queue parameters, and Quality of Service Policies controls directly the message queues to obtain the user-defined parameters values., The middleware architecture described in this article is a part of the coordinated project SIDIRELI: Distributed Systems with Limited Resources. Control Kernel and Coordination. Education and Science Department, Spanish Government. CICYT: MICINN: DPI2008-06737-C02-01/02.