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/s1508180801808018101158Gupta, 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