Outlook of Cloud, CPS and IoT in Manufacturing

  • Lihui WangEmail author
  • Xi Vincent Wang


This chapter presents a summary of the current status and the latest advancement of Cloud technology, Cyber-Physical Systems (CPS) and Internet of Things (IoT) in manufacturing, where CPS is treated as a main thread. In order to understand CPS and its future potential in manufacturing, definitions and characteristics of CPS are explained and compared with cloud manufacturing and IoT concepts. Research and application potentials are outlined to highlight the latest advancement and future directions in the manufacturing domain. Cloud-based CPS shows great promise in factories of the future in the areas of future trends as identified at the end of this chapter.


  1. 1.
    A. Patnaik, S. Biswas, S.S. Mahapatra, An evolutionary approach to parameter optimisation of submerged arc welding in the hardfacing process. Int. J. Manuf. Res. 2, 462–483 (2007)CrossRefGoogle Scholar
  2. 2.
    E.A. Lee, Cyber-physical systems-are computing foundations adequate. Position paper for NSF workshop on cyber-physical systems: research motivation, techniques and roadmap (2006)Google Scholar
  3. 3.
    M. Grimheden, M. Hanson, What is Mechatronics? Proposing a Didactical Approach to Mechatronics. 1st Balt. Sea Workshop on Education in Mechatronics (2001)Google Scholar
  4. 4.
    Lee EA, Cyber physical systems: design challenges, in 11th IEEE international symposium on Object oriented real-time distributed computing (isorc), pp. 363–369Google Scholar
  5. 5.
    UC Regents, Cyber-physical systems (2009).
  6. 6.
    L. Monostori, Cyber-physical production systems: roots, expectations and R&D challenges. Procedia CIRP. 17, 9–13 (2014)CrossRefGoogle Scholar
  7. 7.
    CODESYS, Industrial IEC 61131-3 PLC Programming (2017). Available from:
  8. 8.
    M. Onori, Oliveira J. Barata, Outlook report on the future of European assembly automation. Assem. Autom. 30, 7–31 (2010)CrossRefGoogle Scholar
  9. 9.
    H. Van Brussel, J. Wyns, P. Valckenaers, L. Bongaerts, P. Peeters, Reference architecture for holonic manufacturing systems: PROSA. Comput. Ind. 37, 255–274 (1998)CrossRefGoogle Scholar
  10. 10.
    H.A. ElMaraghy, Changing and evolving products and systems–models and enablers. Chang. Reconfigurable Manuf. Syst. 25–45 (2009)Google Scholar
  11. 11.
    M. Onori, J. Barata, Evolvable production systems: mechatronic production equipment with process-based distributed control. IFAC Proc. Vol. 42, 80–85 (2009)CrossRefGoogle Scholar
  12. 12.
    A.A. Rizzi, J. Gowdy, R.L. Hollis, Distributed coordination in modular precision assembly systems. Int. J. Rob. Res. 20, 819–838 (2001)CrossRefGoogle Scholar
  13. 13.
    F.P. Maturana, D.H. Norrie, Multi-agent mediator architecture for distributed manufacturing. J. Intell. Manuf. 7, 257–270 (1996)CrossRefGoogle Scholar
  14. 14.
    A.W. Colombo, R. Schoop, R. Neubert, An agent-based intelligent control platform for industrial holonic manufacturing systems. IEEE Trans. Ind. Electron. IEEE 53, 322–337 (2006)CrossRefGoogle Scholar
  15. 15.
    L. Monostori, J. Váncza, S.R.T. Kumara, Agent-based systems for manufacturing. Ann. CIRP. 55, 697–720 (2006)CrossRefGoogle Scholar
  16. 16.
    L. Ribeiro, J. Barata, J. Ferreira, An agent-based interaction-oriented shop floor to support emergent diagnosis, in 8th IEEE International Conference IEEE Industrial Informatics (INDIN) (2010), pp. 189–194Google Scholar
  17. 17.
    L. Wang, M. Törngren, M. Onori, Current status and advancement of cyber-physical systems in manufacturing. J. Manuf. Syst. 37(2), 517–527 (2015)CrossRefGoogle Scholar
  18. 18.
    TerraSwarm, The TerraSwarm Research Center [Internet] (2017). Available from:
  19. 19.
    The Whitehouse, President Obama Launches Advanced Manufacturing Partnership Steering Committee “2.0” [Internet]. Off. Press Secr. 2013. Available from:
  20. 20.
    IIC, Industrial Internet Consortium (2010).
  21. 21.
    NSF, Cyber-Physical Systems [Internet] (2013). Available from:
  22. 22.
    H. Kagermann, J. Helbig, A. Hellinger, W. Wahlster, Recommendations for implementing the strategic initiative INDUSTRIE 4.0: securing the future of German manufacturing industry; final report of the Industrie 4.0 Working Group. Forschungsunion (2013)Google Scholar
  23. 23.
    EU, HORIZON 2010—The EU Framework Programme for Research and Innovation [Internet] (2014). Available from:
  24. 24.
    L. Wang, B. Wong, W. Shen, S. Lang, A Java 3D-enabled cyber workspace. Commun. ACM 45, 45–49 (2002)Google Scholar
  25. 25.
    L. Wang, Wise-ShopFloor: an integrated approach for web-based collaborative manufacturing. Syst. Man Cybern. Part C Appl. Rev. IEEE Trans. 38, 562–573 (2008)Google Scholar
  26. 26.
    CyPhERS, Cyber-Physical European Roadmap and Strategy (2013).
  27. 27.
    A.S. Vincentelli, CPS week keynote. Commun. Dur. keynote present by Alberto Sangiovanni Vincentelli Int. Conf. CPS CPS Week 2014Google Scholar
  28. 28.
    L. Takayama, W. Ju, C. Nass, Beyond dirty, dangerous and dull: what everyday people think robots should do, in Proceedings of 3rd ACM/IEEE International Conferences of Humun Robot Interaction ACM (2008), pp. 25–32Google Scholar
  29. 29.
    R. Parasuraman, T.B. Sheridan, C.D. Wickens, A model for types and levels of human interaction with automation. IEEE Trans. Syst. Man. Cybern. A Syst. Humans. IEEE 30, 286–297 (2000)Google Scholar
  30. 30.
    D. Mourtzis, M. Doukas, C. Vandera, Mobile apps for product customisation and design of manufacturing networks. Manuf. Lett. 2, 30–34 (2014)CrossRefGoogle Scholar
  31. 31.
    H.A. Thompson, Input paper analysis of the state-of-the-art and future challenges in the application domain related to WG1. Deliverable of EU CPSoS Project (2014).
  32. 32.
    K. Nagorny, A.W. Colombo, U. Schmidtmann, A service- and multi-agent-oriented manufacturing automation architecture: an IEC 62264 level 2 compliant implementation. Comput. Ind. 63, 813–823 (2012)CrossRefGoogle Scholar
  33. 33.
    K. Ashton, That “internet of things” thing. RFiD J. 22 (2011)Google Scholar
  34. 34.
    P. Mell, T. Grance, The NIST definition of cloud computing. NIST Spec. Publ. 800, 7 (2011)Google Scholar
  35. 35.
    L. Wang, A. Mohammed, M. Onori, Remote robotic assembly guided by 3D models linking to a real robot. CIRP Ann. Technol. 63, 1–4 (2014)CrossRefGoogle Scholar
  36. 36.
    M. Törngren, S. Bensalem, V. Cengarle, J. McDermid, R. Passerone, A. Sangiovanni Vincentelli, CPS: Significance, Challenges and Opportunities. (CyPhERS-Cyber-Physical European Roadmap and Strategy, co-funded by the European Commission under the Seventh Framework Programme, 2014)Google Scholar
  37. 37.
    D. Goodin, Backdoor in computer controls opens critical infrastructure to hackers. Ars Tecnica.
  38. 38.
    K.J. Higgins, Utilities facing brute-force attack threat [Internet]. Available from:
  39. 39.
    Symantec, The Shamoon Attacks [Internet] (2012). Available from:
  40. 40.
    StuxNet, All about Stuxnet [Internet]. Available from:
  41. 41.
    S.J. Dunlap, Timing-based side channel analysis for anomaly detection in the industrial control system environment (Air Force Institute of Technology, 2013)Google Scholar
  42. 42.
    Schuett CD, Programmable logic controller modification attacks for use in detection analysis (Air Force Institute of Technology, Wright-Patterson Afb Oh Graduate School of Engineering and Management, 2014)Google Scholar
  43. 43.
    Williams PM, Distinguishing internet-facing ICS devices using PLC programming information (Air Force Institute of Technology, Wright-Patterson Afb Oh Graduate School of Engineering and Management, 2014)Google Scholar
  44. 44.
    V. Cengarle, S. Bensalem, J. McDermid, R. Passerone, A. Sangiovanni-Vincentelli, M. Törngren, Characteristics, capabilities, potential applications of cyber-physical systems: a preliminary analysis. D2.1 CyPhERS FP7 Proj. (2013)Google Scholar
  45. 45.
    A. Maffei, M. Onori, Evolvable production systems: environment for new business models. Key Eng. Mater. Trans. Tech. Publ. 1592–1597 (2011)Google Scholar
  46. 46.
    L. Wang, An overview of function block enabled adaptive process planning for machining. J. Manuf. Syst. 35, 10–25 (2015)CrossRefGoogle Scholar
  47. 47.
    L. Wang, Machine availability monitoring and machining process planning towards cloud manufacturing. CIRP J. Manuf. Sci. Technol. 6, 263–273 (2013)CrossRefGoogle Scholar
  48. 48.
    IEC, IEC 61499. Function Blocks for Industrial-process Measurement and Control Systems-Part 1: Architecture (IEC, Geneva 2005)Google Scholar
  49. 49.
    P. Ferreira, N. Lohse, S. Ratchev, Multi-agent architecture for reconfiguration of precision modular assembly systems. Precis. Assem. Technol. Syst. 247–254 (2010)Google Scholar
  50. 50.
    L. Wang, R. Sams, M. Verner, F. Xi, Integrating Java 3D model and sensor data for remote monitoring and control. Robot. Comput. Integr. Manuf. 19, 13–19 (2003)CrossRefGoogle Scholar
  51. 51.
    L. Wang, A. Mohammed, M. Onori, Remote robotic assembly guided by 3d models linking to a real robot. CIRP Ann. Manuf. Technol. 63(1), 1–4 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of Production EngineeringKTH Royal Institute of TechnologyStockholmSweden

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