QoS-Based Middleware Architecture for Distributed Control Systems

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-85863-8_70This paper presents an implementation of a middleware architecture to control distributed systems. The main objective is providing a QoS level between the communications layer and the control layer. This architecture is based on the use of a hierarchical communications structure called logical namespace tree and a structured set of control processes interconnected, called logical sensors graph . This architecture is named Frame Sensor Adapter Control (FSA-Ctrl). In this architecture communication layer and control layer can manage the QoS policies. The communication layer is based on the Data Distribution Service (DDS), a standard proposed by Object Management Group (OMG). Control layer is derived from the Sensor Web Enablement (SWE) model proposed by Open Geospatial Consortium (OGC). Middleware components use messages queues to manage components QoS requirements. By means of QoS policies, control components can take important decisions about distributed questions, like components mobility or information redundancy detection.The architecture described in this article is a part of the coordinated project KERTROL: Kernel control on embedded system strongly connected. Education and Science Department, Spanish Government. CICYT: DPI2005-09327-C02-01/02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2009). QoS-Based Middleware Architecture for Distributed Control Systems. En International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Springer. 587-595. https://doi.org/10.1007/978-3-540-85863-8_70S587595Matteucci, M.: Publish/Subscribe Middleware for Robotics: Requirements and State of the Art. Technical Report N 2003.3, Politecnico di Milano, Milano, Italy (2003)OMG. Data Distribution Service for Real-Time Systems, v1.1. Document formal/2005-12-04 (2005)Botts, M., Percivall, G., Reed, C., Davidson, J. (eds.): OGC. Sensor Web Enablement: Overview and High Level Architecture. OGC White Paper. OGC 06-050r2 (2006)Coulouris, G., Dollimore, J., Kindberg, T.: Distributed systems, concepts and design, 3rd edn. Addison-Wesley, Reading (2001)OMG. Real-Time Corba Specification version 1.1. Document formal /02-08-02 (2002)FIPA. Specfication. Part 2, Agent Communication Language. Foundation for Intelligent Physical Agents (1997)Hapner, M., Sharma, R., Fialli, J., Stout, K.: JMS specification, vol. 1.1. Sun Microsystems Inc., Santa Clara (2002)Pardo-Castellote, G.: OMG Data-Distribution Service: architectural overview. In: Proceedings of 23rd International Conference on Distributed Computing Systems Workshops, Providence, USA, vol. 19-22, pp. 200–206 (2003)Vogel, 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, pp. 1–37 (1998)Botts, M., Percivall, G., Reed, C., Davidson, J.: OGC. Sensor Web Enablement: Overview and High Level Architecture. OpenGIS Consortium Inc. (2006)Posadas, J.L., Perez, P., Simo, J.E., Benet, G., Blanes, F.: Communication structure for sensory data in mobile robots. Engineering Applications of Artificial Intelligence 15(3-4), 341–350 (2002)Poza, J.L., Posadas, J.L., Simó, J.E., Benet, G.: Hierarchical communication system to manage maps in mobile robot navigation. In: Proceedings of International Conference on Automation, Control and Instrumentation, Valencia, Spain (2006)Poza, J.L., Posadas, J.L., Simó, J.E.: Distributed agent specification to an Intelligent Control Architecture. In: 6th International Workshop on Practical Applications of Agents and Multiagent Systems, Salamanca, Spain (in press, 2007

Adding an ontology to a standardized QoS-based MAS middleware

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-02481-8_12In 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.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2009). Adding an ontology to a standardized QoS-based MAS middleware. En Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living. Springer. 83-90. doi:10.1007/978-3-642-02481-8_12S8390Coulouris, G., Dollimore, J., Kindberg, T.: Distributed systems, concepts and design, 3rd edn. Addison Wesley, Reading (2001)Hapner, M., Sharma, R., Fialli, J., Stout, K.: JMS specification, vol. 1.1. Sun Microsystems Inc., Santa Clara (2002)Lewis, R.: Advanced Messaging Applications with MSMQ and MQ Series. Que Publishing (1999)OMG. Real-Time Corba Specification version 1.1. Document formal /02-08-02 (2002)FIPA. Specfication. Part 2, Agent Communication Language. Foundation for Intelligent Physical Agents (1997)Vogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multimedia and QoS: A Survey. IEEE Multimedia 2(2), 10–19 (1995)Smith, B.: Beyond concepts, or: Ontology as reality representation. In: Formal Ontology in Information Systems (FOIS 2004), pp. 73–84 (2004)Gruber, T.R.: Toward Principles for the Design of Ontologies Used for Knowledge Sharing. International Journal Human-Computer Studies 43(5-6), 907–928 (1995)Pardo-Castellote, G.: OMG Data-Distribution Service: architectural overview. In: Proceedings of 23rd International Conference on Distributed Computing Systems Workshops, Providence, USA, vols. 19-22, pp. 200–206 (2003)Object Management Group (OMG). Unified Modeling Language Specification, v1.4.2, ISO/IEC 19501 (2001)Poza, J.L., Posadas, J.I., Simó, J.E.: Distributed agent specification to an Intelligent Control Architecture. In: 6th International Workshop on Practical Applications of Agents and Multiagent Systems, Salamanca (2007)Poza, J.L., Posadas, J.l., Simó, J.E.: QoS-based middleware archi-tecture for distributed control systems. In: International Symposium on Distributed Computing and Artificial Intelligence, Salamanca (2008

Quality of service and quality of control based protocol to distribute agents

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-14883-5_10This paper describes an agent s movement protocol. Additionally, a distributed architecture to implement such protocol is presented. The architecture allows the agents to move in accordance with their requirements. The protocol is based on division and fusion of the agents in their basic components called Logical Sensors. The movement of the agents is based on the quality of services (QoS) and quality of control (QoC) parameters that the system can provides. The protocol is used to know the impact that the movement of the agents may have on the system and obtain the equilibrium points where the impact is minimal.The 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.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2010). Quality of service and quality of control based protocol to distribute agents. En Distributed Computing and Artificial Intelligence: 7th International Symposium. Springer. 73-80. doi:10.1007/978-3-642-14883-5_10S7380Posadas, J.L., Poza, J.L., Simó, J.E., Benet, G., Blanes, F.: Agent Based Distributed Architecture for Mobile Robot Control. In: Engineering Applications of Artificial Intelligence, vol. 21(6), pp. 805–823. Pergamon Press Ltd., Oxford (2008)Object Management Group (OMG): Data Distribution Service for Real-Time Systems, v1.1. Document formal / 2005-12-04 (2005)Odum, E.P.: Fundamentals of Ecology, 3rd edn. W.B. Saunders Company, Philadelphia (1971)Aurrecoechea, C., Campbell, A.T., Hauw, L.: A Survey of QoS Architectures. ACM/Springer Verlag Multimedia Systems Journal, Special Issue on QoS Architecture 6(3), 138–151 (1998)Pardo-Castellote, G.O.: Data-Distribution Service: architectural overview. In: Proceedings of 23rd International Conference on Distributed Computing Systems Workshops, vol. 19-22, pp. 200–206 (2003)International Telecommunication Union (ITU). Terms and Definitions Related to Quality of Service and Network Performance Including Dependability. ITU-T Recommendation E.800 (0894) (1994)Foundation for Intelligent Physical Agents. FIPA Quality of Service Ontology Specification, Experimental Doc: XC00094 (2002)Dorf, R.C., Bishop, R.H.: Modern Control Systems, 11th edn. Prentice Hall, Englewood Cliffs (2008)Poza, J.L., Posadas, J.L., Simó, J.E.: Middleware with QoS Support to Control Intelligent Systems. In: 2nd International Conference on Advanced Engineering Computing and Applications in Sciences, ADVCOMP, pp. 211–216 (2008)Bellifemine, F., Poggi, A., Rimassa, G.: Jade: A FIPA-compliant agent framework. In: Proceedings of PAAM 1999, pp. 97–108 (1999)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)Foundation for Intelligent Physical Agents. FIPA Agent Management Specification, Doc: FIPA00023 (2000)Jeong, B., Cho, H., Kulvatunyou, B., Jones, A.: A Multi-Criteria Web Services Composition Problem. In: Proceedings of the IEEE International Conference on Information Reuse and Integration, 2007 (IRI 2007), pp. 379–384. IEEE, Los Alamitos (2007)Poza, J.L., Posadas, J.L., Simó, J.E., Benet, G.: Distributed Agent Specification for an Intelligent control Architecture. In: 6th International Workshop on Practical Applications of Agents and Multiagent Systems. IWPAAMS (2007) ISBN 978-84-611-8858-

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

A New Constructivist AI: From Manual Methods to Self-Constructive Systems

The development of artificial intelligence (AI) systems has to date been largely one of manual labor. This constructionist approach to AI has resulted in systems with limited-domain application and severe performance brittleness. No AI architecture to date incorporates, in a single system, the many features that make natural intelligence general-purpose, including system-wide attention, analogy-making, system-wide learning, and various other complex transversal functions. Going beyond current AI systems will require significantly more complex system architecture than attempted to date. The heavy reliance on direct human specification and intervention in constructionist AI brings severe theoretical and practical limitations to any system built that way. One way to address the challenge of artificial general intelligence (AGI) is replacing a top-down architectural design approach with methods that allow the system to manage its own growth. This calls for a fundamental shift from hand-crafting to self-organizing architectures and self-generated code – what we call a constructivist AI approach, in reference to the self-constructive principles on which it must be based. Methodologies employed for constructivist AI will be very different from today’s software development methods; instead of relying on direct design of mental functions and their implementation in a cog- nitive architecture, they must address the principles – the “seeds” – from which a cognitive architecture can automatically grow. In this paper I describe the argument in detail and examine some of the implications of this impending paradigm shift

Internet of robotic things : converging sensing/actuating, hypoconnectivity, artificial intelligence and IoT Platforms

The Internet of Things (IoT) concept is evolving rapidly and influencing newdevelopments in various application domains, such as the Internet of MobileThings (IoMT), Autonomous Internet of Things (A-IoT), Autonomous Systemof Things (ASoT), Internet of Autonomous Things (IoAT), Internetof Things Clouds (IoT-C) and the Internet of Robotic Things (IoRT) etc.that are progressing/advancing by using IoT technology. The IoT influencerepresents new development and deployment challenges in different areassuch as seamless platform integration, context based cognitive network integration,new mobile sensor/actuator network paradigms, things identification(addressing, naming in IoT) and dynamic things discoverability and manyothers. The IoRT represents new convergence challenges and their need to be addressed, in one side the programmability and the communication ofmultiple heterogeneous mobile/autonomous/robotic things for cooperating,their coordination, configuration, exchange of information, security, safetyand protection. Developments in IoT heterogeneous parallel processing/communication and dynamic systems based on parallelism and concurrencyrequire new ideas for integrating the intelligent “devices”, collaborativerobots (COBOTS), into IoT applications. Dynamic maintainability, selfhealing,self-repair of resources, changing resource state, (re-) configurationand context based IoT systems for service implementation and integrationwith IoT network service composition are of paramount importance whennew “cognitive devices” are becoming active participants in IoT applications.This chapter aims to be an overview of the IoRT concept, technologies,architectures and applications and to provide a comprehensive coverage offuture challenges, developments and applications

Coordination approaches and systems - part I : a strategic perspective

This is the first part of a two-part paper presenting a fundamental review and summary of research of design coordination and cooperation technologies. The theme of this review is aimed at the research conducted within the decision management aspect of design coordination. The focus is therefore on the strategies involved in making decisions and how these strategies are used to satisfy design requirements. The paper reviews research within collaborative and coordinated design, project and workflow management, and, task and organization models. The research reviewed has attempted to identify fundamental coordination mechanisms from different domains, however it is concluded that domain independent mechanisms need to be augmented with domain specific mechanisms to facilitate coordination. Part II is a review of design coordination from an operational perspective

The State-of-the-art of Coordinated Ramp Control with Mixed Traffic Conditions

Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both human-driven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.Comment: 8 pages, 1 figure, IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE - ITSC 201

Vision-Based Lane-Changing Behavior Detection Using Deep Residual Neural Network

Accurate lane localization and lane change detection are crucial in advanced driver assistance systems and autonomous driving systems for safer and more efficient trajectory planning. Conventional localization devices such as Global Positioning System only provide road-level resolution for car navigation, which is incompetent to assist in lane-level decision making. The state of art technique for lane localization is to use Light Detection and Ranging sensors to correct the global localization error and achieve centimeter-level accuracy, but the real-time implementation and popularization for LiDAR is still limited by its computational burden and current cost. As a cost-effective alternative, vision-based lane change detection has been highly regarded for affordable autonomous vehicles to support lane-level localization. A deep learning-based computer vision system is developed to detect the lane change behavior using the images captured by a front-view camera mounted on the vehicle and data from the inertial measurement unit for highway driving. Testing results on real-world driving data have shown that the proposed method is robust with real-time working ability and could achieve around 87% lane change detection accuracy. Compared to the average human reaction to visual stimuli, the proposed computer vision system works 9 times faster, which makes it capable of helping make life-saving decisions in time