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

Autonomic computing architecture for SCADA cyber security

Cognitive computing relates to intelligent computing platforms that are based on the disciplines of artificial intelligence, machine learning, and other innovative technologies. These technologies can be used to design systems that mimic the human brain to learn about their environment and can autonomously predict an impending anomalous situation. IBM first used the term ‘Autonomic Computing’ in 2001 to combat the looming complexity crisis (Ganek and Corbi, 2003). The concept has been inspired by the human biological autonomic system. An autonomic system is self-healing, self-regulating, self-optimising and self-protecting (Ganek and Corbi, 2003). Therefore, the system should be able to protect itself against both malicious attacks and unintended mistakes by the operator

Unattended network operations technology assessment study. Technical support for defining advanced satellite systems concepts

The results are summarized of an unattended network operations technology assessment study for the Space Exploration Initiative (SEI). The scope of the work included: (1) identified possible enhancements due to the proposed Mars communications network; (2) identified network operations on Mars; (3) performed a technology assessment of possible supporting technologies based on current and future approaches to network operations; and (4) developed a plan for the testing and development of these technologies. The most important results obtained are as follows: (1) addition of a third Mars Relay Satellite (MRS) and MRS cross link capabilities will enhance the network's fault tolerance capabilities through improved connectivity; (2) network functions can be divided into the six basic ISO network functional groups; (3) distributed artificial intelligence technologies will augment more traditional network management technologies to form the technological infrastructure of a virtually unattended network; and (4) a great effort is required to bring the current network technology levels for manned space communications up to the level needed for an automated fault tolerance Mars communications network

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