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

Local adjustment and global adaptation of control periods for QoC management of control systems

Linking real-time schedulability directly to the Quality of Control (QoC), the ultimate goal of a control system, a hierarchical feedback QoC management framework with the Fixed Priority (FP) and the Earliest-Deadline-First (EDF) policies as plug-ins is proposed in this paper for real-time control systems with multiple control tasks. It uses a task decomposition model for continuous QoC evaluation even in overload conditions, and then employs heuristic rules to adjust the period of each of the control tasks for QoC improvement. If the total requested workload exceeds the desired value, global adaptation of control periods is triggered for workload maintenance. A sufficient stability condition is derived for a class of control systems with delay and period switching of the heuristic rules. Examples are given to demonstrate the proposed approach

Fault Estimation Schemes of Wireless Networked Control Systems for Real-Time Industrial Applications

Bedingt durch das rasante Wachstum der Mikroelektronik sowie der Informations- und Kommunikationstechnologien wurde viel Aufmerksamkeit der Erforschung von drahtlos vernetzten Regelsystemen (W-NCS) gewidmet. Die Entwicklung der W-NCS schuf neue Herausforderungen für die Technologien zur Fehlerabschätzung (FE) bezüglich Störungen bei der Datenübertragung, wie zum Beispiel Übertragungsverzögerung, Paketverlust und Jitter. Um die Sicherheit und Zuverlässigkeit des Systems zu gewährleisten, ist die Entwicklung eines effektiven FE Ansatzes in vernetzten Systemen von zentraler Bedeutung. Andererseits sollten mit der Ausrichtung auf Anwendungen in der Echtzeit-Industrieautomatisierung die spezifischen Eigenschaften der Netzwerke angemessen berücksichtigt werden. Da die Aufgabe der Übertragung von Messungen und Steuerbefehlen in der Regel über einen Zeitraum deterministisch ist, sollten ein deterministischer Übertragungsmechanismus und die entsprechenden FE Verfahren vorgeschlagen werden. Motiviert durch die weit verbreitete Verwendung von sowohl zentralen und als auch dezentralen Strukturen in industriellen Prozesse, ist die Entwicklung sowohl von zentralen und als auch von dezentralen FE Methoden für W-NCS in der Industrieautomation das primäre Ziel dieser Arbeit. Diese Arbeit widmet sich zuerst der Modellierung der Prozesse und der Kommunikationsstruktur. Für die Modellierung der Kommunikation wird das Medium Access Control (MAC) Protokoll basierend auf dem Mehrfachzugriff im Zeitmultiplex (TDMA) modifiziert, um die Echtzeitfähigkeit zu gewährleisten. Das Prozessmodell wird unter Berücksichtigung der Abtastraten auf Basis der hierarchischen Struktur des W-NCS aufgestellt. Durch die Berücksichtigung der Unsicherheit von Netzwerken und Auswirkungen von Fehlern wird ein linear periodisches (LP) Systemmodell, durch die Integration des Kommunikationsmodells und des Prozessmodells, als Basis für die spätere Entwicklung präsentiert. Die weiteren Untersuchungen konzentrieren sich auf die Entwicklung von FE Modellen für zentrale und dezentrale W-NCS. Um eine erhöhte Robustheit gegen unbekannte Störungen und den Schätzfehler des Anfangszustandes zu erreichen, wird ein zentraler FE Ansatz mit Hilfe des stochastischen Modells im Krein Raum vorgeschlagen. Für die dezentrale FE wird der Algorithmus für jedes Teilsystem implementiert und die Kopplungsbeziehungen zwischen den Teilsystemen entsprechend berücksichtigt. Basierend darauf werden die FE Ansätze mit zwei Arten von Residuensignalen präsentiert, nicht-verteilten Residuen und verteilten Residuen,. Um die Wirksamkeit der entwickelten FE Ansätze darzustellen, wird in dieser Arbeit die Industrieplattform WiNC, zumammen mit einem Dreitanksystem verwendet. Die FE Algorithmen wurden in den drei Datenübertragungsfällen Fehlerfrei, mit Verzögerungen und mit Paketverlust verifiziert, so dass die Robustheit gegenüber einer unvollkommenen Kommunikation demonstriert wird. Darüber hinaus wurde die Leistungsfähigkeit bezüglich Sensor- und Aktuator-FE ausgiebig auf der WiNC Plattform getestet.With the rapid growth of microelectronics, information and communication technologies, much attention has been paid on the research of wireless networked control systems (W-NCSs). The development of W-NCSs raises new challenges in fault estimation (FE) technology regarding to the imperfect data transmission, such as transmission delay, packet loss, jitter and so on. To ensure the system safety and reliability, an effective FE approach over networks is of prime importance to be developed. On the other hand, aiming for the applications on real-time industrial automation, the specific characteristics of network should be properly considered. Since the transmission tasks of measurements and control commands are normally deterministic over a period of time, a deterministic transmission mechanism and the relevant FE scheme should be proposed. Motivated by the widespread popularity of centralized and decentralized structures for industrial processes, development of both centralized and decentralized FE schemes for W-NCSs, which can be applied on industrial automation, is the primary objective of this thesis. This thesis is first dedicated to the modeling of communication and process. For the communication modeling, time division multiple access (TDMA) based medium access control (MAC) protocol is modified to guarantee the real-time performance. The process model is built considering multirate sampling based on the hierarchical structure of W-NCSs. By observing the uncertainty of networks and effects of faults, a linear periodic (LP) system model, which is the integration of communication model and process model, is presented as a basis for the later developments. The further study focuses on the development of FE schemes for both centralized and decentralized W-NCSs. To reach an enhanced robustness against unknown disturbance and initial state estimate error, the centralized FE approach is proposed with the help of stochastic model in Krein space. For decentralized FE, the algorithm is implemented by every sub-system, and the coupling relations between sub-systems should be properly considered. Based on it, the FE approaches are presented with two kinds of residual signals, i.e., non-shared residuals and shared residuals, respectively. To illustrate the effectiveness of the derived FE approaches, an industrial platform WiNC integrated with three-tank system is utilized in this thesis. The FE algorithms have been verified for three data transmission cases, i.e., sampling-based, delay and packet loss, so that the robustness against imperfect communication is demonstrated. Moreover, the performances of sensor and actuator FE have also been tested well on WiNC platform