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Smart Manufacturing Through Cloud-Based Smart Objects and SWE

  • Pablo GiménezEmail author
  • Benjamín Molina
  • Carlos E. Palau
  • Manuel Esteve
  • Jaime Calvo
Chapter
  • 4.2k Downloads
Part of the Internet of Things book series (ITTCC)

Abstract

Smart manufacturing is a key aspect for innovation and competitiveness, and involves several dimensions of the production chain to be analysed, assessed and enhanced within a factory. To target this issue, concepts and ideas behind the IoT (Internet of Things) are applied, so that connected smart entities cooperate in order to achieve broader goals or increase the overall knowledge in the factory through information sharing. Smart entities in the IoT are typically referred as WSNs (Wireless Sensor Networks) that capture physical (real) data and events and produce virtual (digital) information to be processed. Unfortunately, current WSNs have limited interoperability and processing capabilities, reducing the integration degree with existing applications. This chapter proposes a solution for both previous technical challenges within a factory. Interoperability is achieved by means of SWE (Sensor Web Enablement) whereas processing capabilities are provided through virtualizing smart objects in a datacentre, placed commonly in the factory but it could also be located elsewhere, applying cloud-based techniques. The architecture and deployment has been arranged for the specific use case of a manufacturing company and a risk prevention scenario. Experimentation results show that smart objects could be provided at runtime with fine granularity level depending on the tasks to be performed. Moreover, smart objects are able to co-operate forming meta-objects to satisfy global tasks or minimize certain risks. Finally, smart objects are able to encapsulate private (health and/or personal) data that should not be shared with other objects or processes.

Keywords

Smart objects Industrial safety Wireless sensor networks Sensor observation service 

Notes

Acknowledgments

This work has been partially funded by the Spanish Ministry of Industry under the project FASyS (Absolutely Safe and Healthy Factory) grant number CENIT 2009-1034.

References

  1. 1.
    Kortuem, G., Kawsar, F., Fitton, D., Sundramoorthy, V.: Smart objects as building blocks for the Internet of things, internet computing. IEEE 14(1), 44–51 (2010)Google Scholar
  2. 2.
    Schreiber, D., Luyten, K., Mühlhäuser, M., Brdiczka, O., Hartman, M.: Introduction to the special issue on interaction with smart objects. Trans. Interact. Intell. Syst. 3(2), 6 (2013)Google Scholar
  3. 3.
    Fortino, G., Guerrieri, A., Russo, W.: Agent-oriented smart objects development. In: Proceedings of IEEE 16th International Conference on Computer Supported Cooperative Work in Design (CSCWD), pp. 907–912 (2012)Google Scholar
  4. 4.
    Fortino, G., Guerrieri, A., Lacopo, M., Lucia, M., Russo, W.: An Agent-based Middleware for Cooperating Smart Objects. In: Highlights on Practical Applications of Agents and Multi-Agent Systems, Communications in Computer and Information Science (CCIS), Vol. 365, pp. 387–398, Springer (2013)Google Scholar
  5. 5.
    Fortino G., Guerrieri A., Russo W., Savaglio C.: Middlewares for Smart Objects and Smart Environments: Overview and Comparison, in Internet of Things based on Smart Objects: technology, middleware and applications, Springer Series on the Internet of Things (2014)Google Scholar
  6. 6.
    Hartmann, M., Schreiber, D., Mühlhäuser, M.: Workshop on interacting with smart objects. In: Proceedings of the 16th International Conference on Intelligent User Interfaces (IUI ’11), pp. 481–482, New York (2011)Google Scholar
  7. 7.
    Montenegro, G., Kushalnagar, N., Hui, J., Culler, D.: RFC 4944: Transmission of IPv6 Packets over IEEE 802.15.4 Networks, Internet Engineering Task Force (2007)Google Scholar
  8. 8.
    Konstantopoulos, C., Pantziou, G., Gavalas, D., Mpitziopoulos, A., Mamalis, B.: A Rendezvous-based approach enabling energy-efficient sensory data collection with mobile sinks. IEEE Trans. Parallel Distrib. Sys. 23(5), 809–817 (2012)CrossRefGoogle Scholar
  9. 9.
    Liu, A.F., Ma, M., Chen, Z.G., Gui, W.: Energy-hole avoidance routing algorithm for WSN. In: Proceedings of the Fourth International Conference on Natural Computation (ICNC’08), pp. 76–80 (2008)Google Scholar
  10. 10.
    Zhang, W., Wang, Y., Ma, Y.: Research of WSN routing algorithm based on the ant algorithm. In: Proceedings of the 9th International Conference on Electronic Measurement and Instruments (ICEMI’09), pp. 422–426 (2009)Google Scholar
  11. 11.
    Ji, S., Pei, Q., Zeng, Y., Yang, C., Bu, S.: An automated black-box testing approach for WSN security protocols. In: Proceedings of the 7th International Conference on Computational Intelligence and Security, pp. 693–697 (2011)Google Scholar
  12. 12.
    Clark, J., Daigle, G.: The Importance of Simulation Techniques in ITS Research and Analysis. In: Proceedings of the 29th Conference on Winter simulation (WSC ’97) (1997)Google Scholar
  13. 13.
    Wu, H., Luo, Q., Zheng, P., Ni, L.M.: VMNet: realistic emulation of wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 18(2), 277–299 (2007)CrossRefGoogle Scholar
  14. 14.
    The OGC Sensor Web Enablement (SWE), Open Geospatial Consortium (OGC). http://www.opengeospatial.org/ogc/markets-technologies/swe/ (2013)
  15. 15.
    Sensor Observation Service (SOS), Open Geospatial Consortium (OGC). http://www.opengeospatial.org/standards/sos (2013)
  16. 16.
    Giménez, P., Molina, B., Palau, C.E., Esteve M.: Sensor web simulation and testing for the IoT. In: IEEE International conference on Systems, Man, and Cybernetics (IEEE SMC 2013), Manchester, October 2013Google Scholar
  17. 17.
    McFarlane, D., Sarma, S., Chirn, J.L., Wong, C.Y., Ashton, K.: The intelligent product in manufacturing control and management. In: 15th Triennial World Congress IFAC, Barcelona, Spain (2002)Google Scholar
  18. 18.
    Bajic, E.: Ambient services modeling framework for intelligent products, UbiComp 2005. In: Workshop on Smart Object Systems, Tokyo Sept 2005Google Scholar
  19. 19.
    Clayman, S., Galis, A.: INOX: a managed service platform for inter-connected smart objects, ACM CoNext 2011. IoTSP workshop, Tokyo, December 2011Google Scholar
  20. 20.
    Bajic E.: A service-based methodology for RFID-smart objects interactions in supply chain, Int. J. Multimedia Ubiquitous Eng. 4(3), 37–56 (2009)Google Scholar
  21. 21.
    Tarazona, G.M., Espada, J.P., Nunez-Valdez, E.R.: Using collaborative virtual objects based on services to communicate smart objects. In: Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), pp. 456–461 (2013)Google Scholar
  22. 22.
    Rao, B.B.P., Saluia, P., Sharma, N., Mittal, A., Sharma, S.V.: Cloud computing for internet of things & sensing based applications. In: Sixth International Conference on Sensing Technology (ICST), Kolkata (2012)Google Scholar
  23. 23.
    Zhou, J., Leppanen, T., Harjula, E., Ylianttila, M., Ojala, T., Yu, C., Jin, H., Yang, L.T: Cloud things: a common architecture for integrating the internet of things with cloud computing. In: Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD), Whistler (2013)Google Scholar
  24. 24.
    North Sensor Web Community. http://52north.org/communities/sensorweb/ (2013)
  25. 25.
    Levis, P., Lee, N., Welsh, M., Culler, D.: TOSSIM: accurate and scalable simulation of entire TinyOS applications. In: Proceedings of the 1st International Conference on Embedded Networked Sensor Systems (2003)Google Scholar
  26. 26.
    Girod, L., Elson, J., Stathopoulos, T., Lukac, M., Estrin, D.: Emstar: a software environment for developing and deploying wireless sensor networks. In: Proceedings of the 2004 USENIX Technical Conference (2004)Google Scholar
  27. 27.
    Downard, I.: Simulating sensor networks in NS-2, NRL/FR/5522-04-10,073 (http://cs.itd.nrl.navy.mil/pubs/docs/nrlsensorsim04.pdf) (2004)
  28. 28.
    Varga, A., Hornig, R.: An overview of the OMNeT++ simulation environment. In: Proceedings of the 1st International Conference on Simulation Tools and Techniques for Communications, Networks and Systems (Simutools ’08) (2008)Google Scholar
  29. 29.
    Zeng, X., Bagrodia, R., Gerla, M., GloMoSim: a library for parallel simulation of large-scale wireless networks, Parallel and Distributed Simulation, 1998. PADS 98. In: Proceedings of Twelfth Workshop pp. 154, 161, 26–29 (1998)Google Scholar
  30. 30.
    Research & Innovation Industrial Technologies, Public Private Partnerships in Research, Factories of the Future. http://ec.europa.eu/research/industrial_technologies/factories-of-the-future_en.html (2013)
  31. 31.
    Absolutely Safe and Healthy Factory (FASyS) project. http://www.fasys.es/en/ (2013)

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pablo Giménez
    • 1
    Email author
  • Benjamín Molina
    • 1
  • Carlos E. Palau
    • 1
  • Manuel Esteve
    • 1
  • Jaime Calvo
    • 2
  1. 1.Universitat Politècnica de ValènciaValènciaSpain
  2. 2.Universidad de SalamancaSalamancaSpain

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