A survey on quality of service support on middelware-based distributed messaging systems used in multi agent systems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-19934-9_10Messaging systems are widely used in distributed systems to hide the details of the communications mechanism to the multi agents systems. However, the Quality of Service is treated in different way depending on the messaging system used. This article presents a review and further analysis of the quality of service treatment in the mainly messaging systems used in distributed multi agent systems. The review covers the issues related to the purpose of the functions provided and the scope of the quality of service offered by every messaging system. We propose ontology for classifying and decide which parameters are relevant to the user. The results of the analysis and the ontology can be used to select the most suitable messaging system to distributed multi agent architecture and to establish the quality of service requirements in a distributed system.The study 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 and European FEDER found. CICYT: MICINN: DPI2008-06737-C02-01/02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2011). A survey on quality of service support on middelware-based distributed messaging systems used in multi agent systems. En International Symposium on Distributed Computing and Artificial Intelligence. Springer. 77-84. https://doi.org/10.1007/978-3-642-19934-9_10S7784Gaddah, A., Kunz, T.: A survey of middleware paradigms for mobile computing. Technical Report SCE-03-16. Carleton University Systems and Computing Engineering (2003)Foundation for Intelligent Physical Agents, http://www.fipa.org/Java Message Service Specification, http://java.sun.com/products/jms/docs.htmlCommon Object Request Broker Architecture, http://www.corba.org/Data Distribution Service, http://portals.omg.org/dds/Java Agent DEvelopment Framework, http://jade.tilab.com/Agent Oriented Software Pty Ltd., JACK Intelligent Agents: User Guide (1999)Nwana, H., Ndumu, D., Lee, L., Collis, J.: ZEUS: A tool-kit for building distributed multi-agent systems. Applied Artifical Intelligence Journal 13(1), 129–186 (1999)Perdikeas, M.K., Chatzipapadopoulos, F.G., Venieris, I.S., Marino, G.: Mobile Agent Standards and Available Platforms. Computer Networks Journal, Special Issue on ’Mobile Agents in Intelligent Networks and Mobile Communication Systems’ 31(10) (1999)Perrone, P.J., Chaganti, K.: J2EE Developer’s Handbook. Sam’s Publishing, Indianapolis (2003)Apache ActiveMQ, http://activemq.apache.org/IBM WebSphere MQSeries, http://mqseries.net/Object Management Group, http://www.omg.org/RTI Data Distribution Service. RTI corp., http://www.rti.com/OpenSplice DDS. PrismTech Ltd., http://www.prismtech.comVogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multimedia and QoS: A Survey. IEEE Multimedia 2(2), 10–19 (1995)Crawley, E., Nair, R., Rajagopalan, B.: RFC 2386: A Framework for QoS-based Routing in the Internet. IETF Internet Draft, 1–37 (1998)Foundation for Intelligent Physical Agents. FIPA Quality of Service Ontology Specification. Doc: SC00094A (2002)Sun Microsystems, Inc. Java(TM) Message Service Specification Final Release 1.1 (2002)Object Management Group (OMG). The Common Object Request Broker Architecture and Specification. CORBA 2.4.2 (2001

Relationship between quality of control and quality of service in mobile robot navigation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-28765-7_67This article presents the experimental work developed to test the viability and to measure the efficiency of an intelligent control distributed architecture. To do this, a simulated navigation scenario of Braitenberg vehicles has been developed. To test the efficiency, the architecture uses the performance as QoS parameter. The measuring of the quality of the navigation is done through the ITAE QoC parameter. Tested scenarios are: an environment without QoS and QoC man-aging, an environment with a relevant message filtering and an environment with a predictive filtering by the type of control. The results obtained show that some of the processing performed in the control nodes can be moved to the middleware to optimize the robot navigation.The work described in this article is a part of the coordinated project SIDIRELI: (Distributed Systems with Limited Resources) and COBAMI (Mission-Based Control) Education and Science Department, Spanish Government and European FEDER found. MICINN CICYT: SIDIRELI: DPI2008-06737-C02-01/02, COBAMI: DPI2011-28507-C02-02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2012). Relationship between quality of control and quality of service in mobile robot navigation. En Distributed Computing and Artificial Intelligence: 9th International Conference. Springer. 557-564. https://doi.org/10.1007/978-3-642-28765-7_67S557564Vogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multimedia and QoS: A Survey. IEEE Multimedia 2(2), 10–19 (1995)Crawley, E., Nair, R., Rajagopalan, B.: RFC 2386: A Framework for QoS-based Routing in the Internet. IETF Internet Draft, 1–37 (1998)Bradner, S.: RFC 2026: The Internet Standards Process. IETF Internet Draft, sec.10 (1996)Object Management Group (OMG): Data Distribution Service for Real-Time Systems, v1.1. Document formal (April 12, 2005)Poza, J.L., Posadas, J.L., Simó, J.E.: QoS-based middleware architecture for distributed control systems. In: International Symposium on Distributed Computing and Artificial Intelligence. DCAI, Salamanca, Spain (2008)Poza, J.L., Posadas, J.L., Simó, J.E.: A Survey on Quality of Service Support on Middleware-Based Distributed Messaging Systems Used in Multi Agent Systems. In: 9th International Conference on Practical Applications of Agents and Multi-Agent Systems. DCAI, Salamanca, Spain (2011)Dorf, R.C., Bishop, R.H.: Modern Control Systems, 11th edn. Prentice Hall (2008)Soucek, S., Sauter, T.: Quality of Service Concerns in IPBased Control Systems. IEEE Transactions on Industrial Electronics 51(6) (December 2004)Poza, J.L., Posadas, J.L., Simó, J.E.: Multi-Agent Architecture with Support to Quality of Service and Quality of Control. In: 11th International Conference on Intelligent Data Engineering and Automated Learning, Paisley, UK (2010)Braitenberg, V.: Vehicles: Experiments on Synthetic Psychology. MIT Press, Cambridge (1984)Gabel, O., Litz, L.: QoS-adaptive Control in NCS with Variable Delays and Packet Losses – A Heuristic Approach. In: 43rd IEEE Conference on Decision and Control (2004)Poza, J.L., Posadas, J.L., Simó, J.E.: From the Queue to the Quality of Service Policy: A Middleware Implementation. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009, Part II. LNCS, vol. 5518, pp. 432–437. Springer, Heidelberg (2009

Distributed sensor architecture for intelligent control that supports quality of control and quality of service

This paper is part of a study of intelligent architectures for distributed control and communications systems. The study focuses on optimizing control systems by evaluating the performance of middleware through quality of service (QoS) parameters and the optimization of control using Quality of Control (QoC) parameters. The main aim of this work is to study, design, develop, and evaluate a distributed control architecture based on the Data-Distribution Service for Real-Time Systems (DDS) communication standard as proposed by the Object Management Group (OMG). As a result of the study, an architecture called Frame-Sensor-Adapter to Control (FSACtrl) has been developed. FSACtrl provides a model to implement an intelligent distributed Event-Based Control (EBC) system with support to measure QoS and QoC parameters. The novelty consists of using, simultaneously, the measured QoS and QoC parameters to make decisions about the control action with a new method called Event Based Quality Integral Cycle. To validate the architecture, the first five Braitenberg vehicles have been implemented using the FSACtrl architecture. The experimental outcomes, demonstrate the convenience of using jointly QoS and QoC parameters in distributed control systems.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 project "Real time distributed control systems" of the Support Program for Research and Development 2012 UPV (PAID-06-12).Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Simarro Fernández, R.; Benet Gilabert, G. (2015). Distributed sensor architecture for intelligent control that supports quality of control and quality of service. Sensors. 15(3):4700-4733. https://doi.org/10.3390/s150304700S4700473315

Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems

The inclusion of embedded sensors into a networked system provides useful information for many applications. A Distributed Control System (DCS) is one of the clearest examples where processing and communications are constrained by the client s requirements and the capacity of the system. An embedded sensor with advanced processing and communications capabilities supplies high level information, abstracting from the data acquisition process and objects recognition mechanisms. The implementation of an embedded sensor/actuator as a Smart Resource permits clients to access sensor information through distributed network services. Smart resources can offer sensor services as well as computing, communications and peripheral access by implementing a self-aware based adaptation mechanism which adapts the execution profile to the context. On the other hand, information integrity must be ensured when computing processes are dynamically adapted. Therefore, the processing must be adapted to perform tasks in a certain lapse of time but always ensuring a minimum process quality. In the same way, communications must try to reduce the data traffic without excluding relevant information. The main objective of the paper is to present a dynamic configuration mechanism to adapt the sensor processing and communication to the client s requirements in the DCS. This paper describes an implementation of a smart resource based on a Red, Green, Blue, and Depth (RGBD) sensor in order to test the dynamic configuration mechanism presented.This work has been supported by the Spanish Science and Innovation Ministry MICINN under the CICYT project M2C2: "Codiseno de sistemas de control con criticidad mixta basado en misiones" TIN2014-56158-C4-4-P and the Programme for Research and Development PAID of the Polytechnic University of Valencia: UPV-PAID-FPI-2013. The responsibility for the content remains with the authors.Munera Sánchez, E.; Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Blanes Noguera, F. (2015). Dynamic Reconfiguration of a RGBD Sensor Based on QoS and QoC Requirements in Distributed Systems. Sensors. 15(8):18080-18101. https://doi.org/10.3390/s150818080S1808018101158Gupta, R. A., & Mo-Yuen Chow. (2010). Networked Control System: Overview and Research Trends. IEEE Transactions on Industrial Electronics, 57(7), 2527-2535. doi:10.1109/tie.2009.2035462Morales, R., Badesa, F. J., García-Aracil, N., Perez-Vidal, C., & Sabater, J. M. (2012). Distributed Smart Device for Monitoring, Control and Management of Electric Loads in Domotic Environments. Sensors, 12(5), 5212-5224. doi:10.3390/s120505212Zhang, Z. (2012). Microsoft Kinect Sensor and Its Effect. IEEE Multimedia, 19(2), 4-10. doi:10.1109/mmul.2012.24Gonzalez-Jorge, H., Riveiro, B., Vazquez-Fernandez, E., Martínez-Sánchez, J., & Arias, P. (2013). Metrological evaluation of Microsoft Kinect and Asus Xtion sensors. Measurement, 46(6), 1800-1806. doi:10.1016/j.measurement.2013.01.011Pordel, M., & Hellström, T. (2015). Semi-Automatic Image Labelling Using Depth Information. Computers, 4(2), 142-154. doi:10.3390/computers4020142Zuehlke, D. (2010). SmartFactory—Towards a factory-of-things. Annual Reviews in Control, 34(1), 129-138. doi:10.1016/j.arcontrol.2010.02.008Wang, X., Şekercioğlu, Y., & Drummond, T. (2014). Vision-Based Cooperative Pose Estimation for Localization in Multi-Robot Systems Equipped with RGB-D Cameras. Robotics, 4(1), 1-22. doi:10.3390/robotics4010001Gil, P., Kisler, T., García, G. J., Jara, C. A., & Corrales, J. A. (2013). Calibración de cámaras de tiempo de vuelo: Ajuste adaptativo del tiempo de integración y análisis de la frecuencia de modulación. Revista Iberoamericana de Automática e Informática Industrial RIAI, 10(4), 453-464. doi:10.1016/j.riai.2013.08.002Castrillón-Santan, M., Lorenzo-Navarro, J., & Hernández-Sosa, D. (2014). Conteo de personas con un sensor RGBD comercial. Revista Iberoamericana de Automática e Informática Industrial RIAI, 11(3), 348-357. doi:10.1016/j.riai.2014.05.006Vogel, A., Kerherve, B., von Bochmann, G., & Gecsei, J. (1995). Distributed multimedia and QOS: a survey. IEEE Multimedia, 2(2), 10-19. doi:10.1109/93.388195Eugster, P. T., Felber, P. A., Guerraoui, R., & Kermarrec, A.-M. (2003). The many faces of publish/subscribe. ACM Computing Surveys, 35(2), 114-131. doi:10.1145/857076.857078Aurrecoechea, C., Campbell, A. T., & Hauw, L. (1998). A survey of QoS architectures. Multimedia Systems, 6(3), 138-151. doi:10.1007/s005300050083Xu, W., Zhou, Z., Pham, D. T., Liu, Q., Ji, C., & Meng, W. (2012). Quality of service in manufacturing networks: a service framework and its implementation. The International Journal of Advanced Manufacturing Technology, 63(9-12), 1227-1237. doi:10.1007/s00170-012-3965-yKang, W., Son, S. H., & Stankovic, J. A. (2012). Design, Implementation, and Evaluation of a QoS-Aware Real-Time Embedded Database. IEEE Transactions on Computers, 61(1), 45-59. doi:10.1109/tc.2010.240Poza-Lujan, J.-L., Posadas-Yagüe, J.-L., Simó-Ten, J.-E., Simarro, R., & Benet, G. (2015). Distributed Sensor Architecture for Intelligent Control that Supports Quality of Control and Quality of Service. Sensors, 15(3), 4700-4733. doi:10.3390/s150304700Manzoor, A., Truong, H.-L., & Dustdar, S. (2014). Quality of Context: models and applications for context-aware systems in pervasive environments. The Knowledge Engineering Review, 29(2), 154-170. doi:10.1017/s0269888914000034Cardellini, V., Casalicchio, E., Grassi, V., Iannucci, S., Presti, F. L., & Mirandola, R. (2012). MOSES: A Framework for QoS Driven Runtime Adaptation of Service-Oriented Systems. IEEE Transactions on Software Engineering, 38(5), 1138-1159. doi:10.1109/tse.2011.68Nogueira, L., Pinho, L. M., & Coelho, J. (2012). A feedback-based decentralised coordination model for distributed open real-time systems. Journal of Systems and Software, 85(9), 2145-2159. doi:10.1016/j.jss.2012.04.033del-Hoyo, R., Martín-del-Brío, B., Medrano, N., & Fernández-Navajas, J. (2009). Computational intelligence tools for next generation quality of service management. Neurocomputing, 72(16-18), 3631-3639. doi:10.1016/j.neucom.2009.01.016Tian, Y.-C., Jiang, X., Levy, D. C., & Agrawala, A. (2012). Local Adjustment and Global Adaptation of Control Periods for QoC Management of Control Systems. IEEE Transactions on Control Systems Technology, 20(3), 846-854. doi:10.1109/tcst.2011.2141133Vilalta, R., & Drissi, Y. (2002). Artificial Intelligence Review, 18(2), 77-95. doi:10.1023/a:1019956318069Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. doi:10.1007/bf00994018Yélamos, I., Escudero, G., Graells, M., & Puigjaner, L. (2009). Performance assessment of a novel fault diagnosis system based on support vector machines. Computers & Chemical Engineering, 33(1), 244-255. doi:10.1016/j.compchemeng.2008.08.008Zhang, X., Qiu, D., & Chen, F. (2015). Support vector machine with parameter optimization by a novel hybrid method and its application to fault diagnosis. Neurocomputing, 149, 641-651. doi:10.1016/j.neucom.2014.08.010Iplikci, S. (2010). Support vector machines based neuro-fuzzy control of nonlinear systems. Neurocomputing, 73(10-12), 2097-2107. doi:10.1016/j.neucom.2010.02.008Ferrari, P., Flammini, A., & Sisinni, E. (2011). New Architecture for a Wireless Smart Sensor Based on a Software-Defined Radio. IEEE Transactions on Instrumentation and Measurement, 60(6), 2133-2141. doi:10.1109/tim.2011.2117090Munera Sánchez, E., Muñoz Alcobendas, M., Blanes Noguera, J., Benet Gilabert, G., & Simó Ten, J. (2013). A Reliability-Based Particle Filter for Humanoid Robot Self-Localization in RoboCup Standard Platform League. Sensors, 13(11), 14954-14983. doi:10.3390/s131114954Adams, R., & Bischof, L. (1994). Seeded region growing. IEEE Transactions on Pattern Analysis and Machine Intelligence, 16(6), 641-647. doi:10.1109/34.295913JIMÉNEZ-GARCÍA, J.-L., BASELGA-MASIA, D., POZA-LUJÁN, J.-L., MUNERA, E., POSADAS-YAGÜE, J.-L., & SIMÓ-TEN, J.-E. (2014). Smart device definition and application on embedded system: performance and optimi-zation on a RGBD sensor. ADCAIJ: ADVANCES IN DISTRIBUTED COMPUTING AND ARTIFICIAL INTELLIGENCE JOURNAL, 3(8), 46. doi:10.14201/adcaij2014384655Feng-Li Lian, Moyne, J., & Tilbury, D. (2002). Network design consideration for distributed control systems. IEEE Transactions on Control Systems Technology, 10(2), 297-307. doi:10.1109/87.98707

MISSION-ORIENTED HETEROGENEOUS ROBOT COOPERATION BASED ON SMART RESOURCES EXECUTION

Home environments are changing as more technological devices are used to improve daily life. The growing demand for high technology in our homes means that robot integration will soon arrive. Home devices are evolving in a connected paradigm in which data flows to perform efficient home task management. Heterogeneous home robots connected in a network can establish a workflow that complements their capabilities and so increases performance within a mission execution. This work addresses the definition and requirements of a robot-group mission in the home context. The proposed solution relies on a network of smart resources, which are defined as cyber-physical systems that provide high-level service execution. Firstly, control middleware architecture is introduced as the execution base for the Smart resources. Next, the Smart resource topology and its integration within a robotic platform are addressed. Services supplied by Smart resources manage their execution through a robot behavior architecture. Robot behavior execution is hierarchically organized through a mission definition that can be established as an individual or collective approach. Environment model and interaction tasks characterize the operation capabilities of each robot within a mission. Mission goal achievement in a heterogeneous group is enhanced through the complement of the interaction capabilities of each robot. To offer a clearer explanation, a full use case is presented in which two robots cooperate to execute a mission and the previously detailed steps are evaluated. Finally, some of the obtained results are discussed as conclusions and future works is introduced.Los entornos domésticos se encuentran sometidos a un proceso de cambio gracias al empleo de dispositivos tecnológicos que mejoran la calidad de vida de las personas. La creciente demanda de alta tecnología en los hogares señala una próxima incorporación de la robótica de servicio. Los dispositivos domésticos están evolucionando hacia un paradigma de conexión en el cual la información fluye para ofrecer una gestión más eficiente. En este entorno, robots heterogéneos conectados a la red pueden establecer un flujo de trabajo que ofreciendo nuevas soluciones y incrementando la eficiencia en la ejecución de tareas. Este trabajo aborda la definición y los requisitos necesarios para la ejecución de misiones en grupos de robots heterogéneos en entornos domésticos. La solución propuesta se apoya en una red de Smart resources, que son definidos como sistemas ciber-físicos que proporcionan servicios de alto nivel. En primer lugar, se presenta la arquitectura del middleware de control en la cual se basa la ejecución de los Smart resources. A continuación se detalla la topología de los Smart resources, así como su integración en plataformas robóticas. Los servicios proporcionados por los Smart resources gestionan su ejecución mediante una arquitectura de comportamientos para robots. La ejecución de estos comportamientos se organiza de forma jerárquica mediante la definición de una misión con un objetivo establecido de forma individual o colectiva a un grupo de robots. Dentro de una misión, las tareas de modelado e interacción con el entorno define las capacidades de operación de los robots dentro de una misión. Mediante la integración de un grupo heterogéneo de robots sus diversas capacidades son complementadas para el logro un objetivo común. A fin de caracterizar esta propuesta, los mecanismos presentados en este documento se evaluarán en detalle a lo largo de una serie experimentos en los cuales un grupo de robots heterogéneos ejecutan una misión colaborativa para alcanzar un objetivo común. Finalmente, los resultados serán discutidos a modo de conclusiones dando lugar el establecimiento de un trabajo futuro.Els entorns domèstics es troben sotmesos a un procés de canvi gràcies a l'ocupació de dispositius tecnològics que milloren la qualitat de vida de les persones. La creixent demanda d'alta tecnologia a les llars assenyala una propera incorporació de la robòtica de servei. Els dispositius domèstics estan evolucionant cap a un paradigma de connexió en el qual la informació flueix per oferir una gestió més eficient. En aquest entorn, robots heterogenis connectats a la xarxa poden establir un flux de treball que ofereix noves solucions i incrementant l'eficiència en l'execució de tasques. Aquest treball aborda la definició i els requisits necessaris per a l'execució de missions en grups de robots heterogenis en entorns domèstics. La solució proposada es recolza en una xarxa de Smart resources, que són definits com a sistemes ciber-físics que proporcionen serveis d'alt nivell. En primer lloc, es presenta l'arquitectura del middleware de control en la qual es basa l'execució dels Smart resources. A continuació es detalla la tipologia dels Smart resources, així com la seva integració en plataformes robòtiques. Els serveis proporcionats pels Smart resources gestionen la seva execució mitjançant una arquitectura de comportaments per a robots. L'execució d'aquests comportaments s'organitza de forma jeràrquica mitjançant la definició d'una missió amb un objectiu establert de forma individual o col·lectiva a un grup de robots. Dins d'una missió, les tasques de modelatge i interacció amb l'entorn defineix les capacitats d'operació dels robots dins d'una missió. Mitjançant la integració d'un grup heterogeni de robots seves diverses capacitats són complementades per a l'assoliment un objectiu comú. Per tal de caracteritzar aquesta proposta, els mecanismes presentats en aquest document s'avaluaran en detall mitjançant d'una sèrie experiments en els quals un grup de robots heterogenis executen una missió col·laborativa per aconseguir un objectiu comú. Finalment, els resultats seran discutits a manera de conclusions donant lloc a l'establiment d'un treball futur.Munera Sánchez, E. (2017). MISSION-ORIENTED HETEROGENEOUS ROBOT COOPERATION BASED ON SMART RESOURCES EXECUTION [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/88404TESI

Propuesta de arquitectura distribuida de control inteligente basada en políticas de calidad de servicio

La tesis se enmarca en el estudio de las arquitecturas inteligentes de control distribuido y de los sistemas de comunicaciones empleados, más concretamente el trabajo se centra en la optimización del sistema de control por medio de la evaluación del rendimiento en el middleware a través de los parámetros de calidad de servicio y de la optimización del control empleando políticas de calidad de servicio. El principal objetivo de este trabajo ha sido estudiar, diseñar, desarrollar y evaluar una arquitectura de control distribuido, basándose en el estándar de comunicaciones Data-Distribution Service for Real-Time Systems (DDS) propuesto por la organización Object Management Group (OMG). Como aportación principal de la tesis se propone el diseño de una arquitectura distribuida de control inteligente que de soporte a la QoS, tanto en la medición por medio de los parámetros, como en la gestión por medio de las políticas de QoS. Las políticas deben permitir la variación de las características de la comunicación en función de los requisitos de control, expresados estos últimos por medio de los parámetros de QoC. A la arquitectura desarrollada se le ha llamado FSACtrl. Para determinar los requisitos de la arquitectura FSACtrl, se han estudiado las revisiones de los autores más relevantes acerca de las características más destacadas de las arquitecturas distribuidas de sistemas de control. A partir de estas características se han diseñado los elementos de la arquitectura FSACtrl. Los elementos que dan soporte a las comunicaciones se han basado en los del estándar DDS de la OMG, mientras que los elementos de control se han basado en el estándar Sensor Web Enablement (SWE) del Open Geospatial Consortium (OGC). Para la validación de la arquitectura se ha implementado un entorno de diseño y simulación del control.Poza Luján, JL. (2012). Propuesta de arquitectura distribuida de control inteligente basada en políticas de calidad de servicio [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/14674Palanci