Skip to main content
SpringerLink
Log in

The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action

  • Chapter
  • First Online:
Digital Twin Technologies and Smart Cities

Part of the book series: Internet of Things ((ITTCC))

Abstract

Digital twins, Internet of Things (IoT), block chains, and Artificial Intelligence (AI) may redefine our imagination and future vision of globalization. Digital Twin will likely affect most of the enterprises worldwide as it duplicates the physical model for remote monitoring, viewing, and controlling based on the digital format. It is actually the living model of the physical system which continuously adapts to operational changes based on the real-time data from various IoT sensors and devices and forecasts the future of the corresponding physical counterparts with the help of machine learning/artificial intelligence. We have investigated the architecture, applications, and challenges in the implementation of digital twin with IoT capabilities. Some of the major research areas like big data and cloud, data fusion, and security in digital twins have been explored. AI facilitates the development of new models and technology systems in the domain of intelligent manufacturing.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Tao, F. and Qi, Q.: New IT driven service-oriented smart manufacturing: framework and characteristics. IEEE Trans. Syst., Man, Cybern. Syst. (2017)

    Google Scholar 

  2. Mourtzis, D., Vlachou, E., Milas, N.: Industrial Big Data as a result of IoT adoption in manufacturing. Procedia CIRP. 55, 290–295 (2016)

    Article  Google Scholar 

  3. Gantz, J. and Reinsel, D.: The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the Far East. IDC iView: IDC Analyze the future. 1–16 (2012)

    Google Scholar 

  4. Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A., Khan, S.U.: The rise of big data on cloud computing: Review and open research issues. Inf. Syst. 47, 98–115 (2015)

    Article  Google Scholar 

  5. Yi, S., Li, C. Li, Q.: A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 workshop on mobile big data, ACM, pp. 37–42 (2015)

    Google Scholar 

  6. Leng, J., Jiang, P.: Dynamic scheduling in RFID-driven discrete manufacturing system by using multi-layer network metrics as heuristic information. J. Intell. Manuf. 1–16 (2017)

    Google Scholar 

  7. Wang, S., Wan, J., Zhang, D., Li, D., Zhang, C.: Towards smart factory for industry 4.0: a self-organized multi-agent system with big data-based feedback and coordination. Comput. Netw. 101, 158–168 (2016)

    Article  Google Scholar 

  8. Wang, S., Wan, J., Li, D., Zhang, C.: Implementing smart factory of industry 4.0: an outlook. Int. J. Distrib. Sens. Netw. 12(1), 3159805 (2016)

    Article  Google Scholar 

  9. Xu, Y., Sun, Y., Wan, J., Liu, X., Song, Z.: Industrial big data for fault diagnosis: Taxonomy, review, and applications. IEEE Access. 5, 17368–17380 (2017)

    Article  Google Scholar 

  10. Wan, J., Tang, S., Li, D., Wang, S., Liu, C., Abbas, H., Vasilakos, A.V.: A manufacturing big data solution for active preventive maintenance. IEEE Trans. Industr. Inf. 13(4), 2039–2047 (2017)

    Article  Google Scholar 

  11. Grieves, M.: Digital twin: manufacturing excellence through virtual factory replication. White paper. (2014)

    Google Scholar 

  12. Constante, T.A.D.S.L.: Contribution for a Simulation Framework for Designing and Evaluating Manufacturing Systems. (2018)

    Google Scholar 

  13. Tao, F., Cheng, J., Qi, Q., Zhang, M., Zhang, H., Sui, F.: Digital twin-driven product design, manufacturing and service with big data. Int. J. Adv. Manuf. Technol. 94(9–12), 3563–3576 (2018)

    Article  Google Scholar 

  14. Machine-to-Machine Communications (M2M): Impact of smart city activity on IoT environment, european telecommunications standards institute (ETSI). Sophia Antipolis, France (2015)

    Google Scholar 

  15. Schuh, G., Anderl, R., Gausemeier, J., Hompel, M.T., Wahlster W.: Industrie 4.0 maturity index. Managing the digital transformation of companies. Munich: Herbert Utz (2017)

    Google Scholar 

  16. Ribeiro, L., Björkman, M.: Transitioning from standard automation solutions to cyber-physical production systems: an assessment of critical conceptual and technical challenges. IEEE Syst. J. 1–13 (2017)

    Google Scholar 

  17. Qin, J., Liu, Y., Grosvenor, R.: A categorical framework of manufacturing for industry 4.0 and beyond. Procedia Cirp 52:173–178 (2016)

    Article  Google Scholar 

  18. Uhlemann, T.H.J., Lehmann, C., Steinhilper, R.: The digital twin: realizing the cyber-physical production system for industry 4.0. Procedia Cirp. 61:335–340 (2017)

    Article  Google Scholar 

  19. Tuegel, E.J., Ingraffea, A.R., Eason, T.G., Spottswood, S.M.: Int. J. Aerosp. Eng. 154798:14 (2011)

    Google Scholar 

  20. Grieves, M.: Digital twin: manufacturing excellence through virtual factory replication. White Pap. (2014)

    Google Scholar 

  21. Qi, Q., Tao, F., Zuo, Y., Zhao, D.: Digital twin service towards smart manufacturing. Procedia CIRP 72(1), 237–242 (2018)

    Article  Google Scholar 

  22. Li, C., Mahadevan, S., Ling, Y., Wang, L., Choze, S.: A dynamic Bayesian network approach for digital twin. In: 19th AIAA Non-Deterministic Approaches Conference, p. 1566 (2017)

    Google Scholar 

  23. Liu, Z., Meyendorf, N., Mrad, N.: The role of data fusion in predictive maintenance using digital twin. In: AIP Conference Proceedings. 1949(1):020023 (2018). AIP Publishing

    Google Scholar 

  24. Schmidt, M.T.: ANSYS Advant. XI:43–45, 2017

    Google Scholar 

  25. https://www.gavstech.com/wp-content/uploads/2017/10/Digital_Twin_Concept.pdf. Last accessed 21 Oct 2018

  26. Grieves, M.: Digital twin: manufacturing excellence through virtual factory replication. White Pap. (2014)

    Google Scholar 

  27. Glaessgen, E., Stargel, D.: The digital twin paradigm for future NASA and US Air Force vehicles. In: 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA 1818 (2012)

    Google Scholar 

  28. Lee, J., Ardakani, H.D., Yang, S., Bagheri, B.: Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia CIRP 38, 3–7 (2015)

    Article  Google Scholar 

  29. Lee, J., Bagheri,B., Kao, H.A.: A cyber-physical systems architecture for industry 4.0-based manufacturing system. Manuf. Lett. 3:18–23 (2015)

    Article  Google Scholar 

  30. Wang, X.V., Wang, L.: A cloud-based production system for information and service integration: an internet of things case study on waste electronics. Enterp. Inf. Syst. 11(7), 952–968 (2017)

    Article  Google Scholar 

  31. Barnaghi, P., Sheth, A., Singh, V., Hauswirth, M.: Physical-cyber-social computing: looking back: looking forward. IEEE Internet Comput. 3, 7–11 (2015)

    Article  Google Scholar 

  32. Hussein, D., Park, S., Han, S.N., Crespi, N.: Dynamic social structure of things: a contextual approach in CPSS. IEEE Internet Comput. 19(3), 12–20 (2015)

    Article  Google Scholar 

  33. Uhlemann, T.H.J., Lehmann, C., Steinhilper, R.: The digital twin: realizing the cyber-physical production system for industry 4.0. Procedia Cirp. 61:335–340 (2017)

    Article  Google Scholar 

  34. Schleich, B., Anwer, N., Mathieu, L., Wartzack, S.: Shaping the digital twin for design and production engineering. CIRP Ann. 66(1), 141–144 (2017)

    Article  Google Scholar 

  35. Lynch, C.: Big data: How do your data grow? Nature 455(7209):28 (2008)

    Article  Google Scholar 

  36. Bandaru, S., Ng, A.H., Deb, K.: Data mining methods for knowledge discovery in multi-objective optimization: Part A-Survey. Expert Syst. Appl. 70, 139–159 (2017)

    Article  Google Scholar 

  37. Bandaru, S., Ng, A.H., Deb, K.: Data mining methods for knowledge discovery in multi-objective optimization: Part B-New developments and applications. Expert Syst. Appl. 70, 119–138 (2017)

    Article  Google Scholar 

  38. Feldmann, S., Vogel-Heuser, B.: Änderungsszenarien in der Automatisierungstechnik–Herausforderungen und interdisziplinäre Auswirkungen. Engineering von der Anforderung bis zum Betrieb 3:95 (2013)

    Google Scholar 

  39. Li, C., Mahadevan, S., Ling, Y., Wang, L., Choze, S.: A dynamic Bayesian network approach for digital twin. In: 19th AIAA Non-Deterministic Approaches Conference, p. 1566 (2017)

    Google Scholar 

  40. Canedo, A.: Industrial IoT lifecycle via digital twins. In: Proceedings of the Eleventh IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis, pp. 29 (2016), ACM

    Google Scholar 

  41. Li, J., Tao, F., Cheng, Y., Zhao, L.: Big data in product lifecycle management. Int. J. Adv. Manuf. Technol. 81(1–4), 667–684 (2015)

    Article  Google Scholar 

  42. Zhang, Y., Zhang, G., Wang, J., Sun, S., Si, S., Yang, T.: Realtime information capturing and integration framework of the Internet of manufacturing things. Int. J. Comput. Integr. Manuf. 28(8), 811–822 (2015)

    Article  Google Scholar 

  43. Kumar, A., Kim, H., Hancke, G.P.: Environmental monitoring systems: a review. IEEE Sens. J. 13(4), 1329–1339 (2013)

    Article  Google Scholar 

  44. Wang, S., Li, Y., Liu, N., Wang, S.: Noisy-data-disposing algorithm of data clean on the attribute level. Comput. Eng. 9, 031 (2005)

    Google Scholar 

  45. Zhu, C., Wang, H., Liu, X., Shu, L., Yang, L.T., Leung, V.C.: A novel sensory data processing framework to integrate sensor networks with mobile cloud. IEEE Syst. J. 10(3), 1125–1136 (2016)

    Article  Google Scholar 

  46. Lin, K.W., Deng, D.J.: A novel parallel algorithm for frequent pattern mining with privacy preserved in cloud computing environments. Int. J. Ad Hoc Ubiquitous Comput. 6(4), 205–215 (2010)

    Article  Google Scholar 

  47. Siddiqa, A., Hashem, I.A.T., Yaqoob, I., Marjani, M., Shamshirband, S., Gani, A., Nasaruddin, F.: A survey of big data management: taxonomy and state-of-the-art. J. Netw. Comput. Appl. 71, 151–166 (2016)

    Article  Google Scholar 

  48. Wright, P., Bourne, D.A.: Manufacturing Intelligence. Addison-Wesley, Boston, MA (1988)

    Google Scholar 

  49. Teti, R., Kumara, S.R.T.: Intelligent computing methods for manufacturing systems. CIRP Ann. 46(2), 629–652 (1997)

    Article  Google Scholar 

  50. Kopacek, P.: Intelligent manufacturing: present state and future trends. J. Intell. Rob. Syst. 26(3–4), 217–229 (1999)

    Article  Google Scholar 

  51. Setoya, H.: History and review of the IMS (Intelligent Manufacturing System). In: 2011 International Conference on Mechatronics and Automation (ICMA), pp. 30–33 (2011)

    Google Scholar 

  52. Shen, W., Norrie, D.H.: Agent-based systems for intelligent manufacturing: a state-of-the-art survey. Knowl. Inf. Syst. 1(2):129–156 (1999)

    Article  Google Scholar 

  53. Mostafaeipour, A., Roy, N.: Implementation of web based technique into the intelligent manufacturing system. Int. J. Comput. Appl. 17(6), 38–43 (2011)

    Google Scholar 

  54. McAfee, A.P.: Enterprise 2.0: the dawn of emergent collaboration. MIT Sloan Manag. Rev. 47(3):21 (2006)

    Article  Google Scholar 

  55. Estellés-Arolas, E., González-Ladrón-De-Guevara, F.: Towards an integrated crowdsourcing definition. J. Inf. Sci. 38(2), 189–200 (2012)

    Article  Google Scholar 

  56. Madejski, J.: Survey of the agent-based approach to intelligent manufacturing. J. Achiev. Mater. Manuf. Eng. 21(1), 67–70 (2007)

    Google Scholar 

  57. Monostori, L., Váncza, J., Kumara, S.R.: Agent-based systems for manufacturing. CIRP Ann. Manuf. Technol. 55(2), 697–720 (2006)

    Article  Google Scholar 

  58. Leitão, P.: Agent-based distributed manufacturing control: a state-of-the-art survey. Eng. Appl. Artif. Intell. 22(7), 979–991 (2009)

    Article  Google Scholar 

  59. Abbas, H.A., Shaheen, S.I., Amin, M.H.: Simple, flexible, and interoperable SCADA system based on agent technology (2015). arXiv preprint arXiv:1509.03214

    Article  Google Scholar 

  60. Lynch, C.: Big data: How do your data grow? Nature 455(7209), 28 (2008)

    Article  Google Scholar 

  61. Louchez, A., Wang, B.: From Smart Manufacturing to Manufacturing Smart (2014)

    Google Scholar 

  62. Leiva, C.: On the Journey to a Smart Manufacturing Revolution. Ind. Week (2015)

    Google Scholar 

  63. Rosen, R., Von Wichert, G., Lo, G., Bettenhausen, K.D.: About the importance of autonomy and digital twins for the future of manufacturing. IFAC-PapersOnLine 48(3), 567–572 (2015)

    Article  Google Scholar 

  64. Tao, F., Cheng, Y., Cheng, J., Zhang, M., Xu, W., Qi, Q.: Theories and technologies for cyber-physical fusion in digital twin shop-floor. Comput. Integr. Manuf. Syst. (2017)

    Google Scholar 

  65. Zhuang, C., Liu, J., Xiong, H., Ding, X., Liu, S., Weng, G.: Connotation, architecture and trends of product digital twin. Comput. Integr. Manuf. Syst. 23(4), 753–768 (2017)

    Google Scholar 

  66. Tuegel, E.J., Ingraffea, A.R., Eason, T.G., Spottswood, S.M.: Reengineering aircraft structural life prediction using a digital twin. Int. J. Aerosp. Eng. (2011)

    Google Scholar 

  67. Gockel, B., Tudor, A., Brandyberry, M., Penmetsa, R., Tuegel, E.: Challenges with structural life forecasting using realistic mission profiles. In: 53rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference 20th AIAA/ASME/AHS Adaptive Structures Conference 14th AIAA, p. 1813 (2012)

    Google Scholar 

  68. Liggins II, M., Hall, D. and Llinas, J. eds.: Handbook of Multisensor Data Fusion: Theory and Practice. CRC press (2017)

    Google Scholar 

  69. Boström, H., Andler, S.F., Brohede, M., Johansson, R., Karlsson, A., Van Laere, J., Niklasson, L., Nilsson, M., Persson, A., Ziemke, T.: On the definition of information fusion as a field of research (2007)

    Google Scholar 

  70. Stevens, S.S.: On the theory of scales of measurement. Science 1946(103), 677–680 (1946)

    Article  Google Scholar 

  71. De, S., Zhou, Y., Larizgoitia Abad, I., Moessner, K.: Cyber–physical–social frameworks for urban big data systems: a survey. Appl. Sci. 7(10):1017 (2017)

    Article  Google Scholar 

  72. Zhang, Y.: GroRec: a group-centric intelligent recommender system integrating social, mobile and big data technologies. IEEE Trans. Serv. Comput. 9(5), 786–795 (2016)

    Article  Google Scholar 

  73. Tuptuk, N., Hailes, S.: Security of smart manufacturing systems. J. Manuf. Syst. 47, 93–106 (2018)

    Article  Google Scholar 

  74. https://industrytoday.com/article/5-types-of-cyber-liabilities-for-manufacturers/ Last accessed 24 Sep 2018)

  75. Piètre-Cambacédès, L., Sitbon, P.: Cryptographic key management for SCADA systems-issues and perspectives. In: 2008 International Conference on Information Security and Assurance, pp. 156–161 (2008). IEEE

    Google Scholar 

  76. Pal, O., Saiwan, S., Jain, P., Saquib, Z., Patel, D.: Cryptographic key management for SCADA system: An architectural framework. In: 2009 International Conference on Advances in Computing, Control, & Telecommunication Technologies, pp. 169–174 (2009). IEEE

    Google Scholar 

  77. Roosta, T., Nilsson, D.K., Lindqvist, U., Valdes, A.: An intrusion detection system for wireless process control systems. In: 5th IEEE International Conference on Mobile Ad Hoc and Sensor Systems, pp. 866–872 (2008). IEEE

    Google Scholar 

  78. Carcano, A., Coletta, A., Guglielmi, M., Masera, M., Fovino, I.N., Trombetta, A.: A multidimensional critical state analysis for detecting intrusions in SCADA systems. IEEE Trans. Industr. Inf. 7(2), 179–186 (2011)

    Article  Google Scholar 

  79. Shin, S., Kwon, T., Jo, G.Y., Park, Y., Rhy, H.: An experimental study of hierarchical intrusion detection for wireless industrial sensor networks. IEEE Trans. Industr. Inf. 6(4), 744–757 (2010)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Engineering, Dubai International Academic City, Amity University Dubai, Dubai, UAE

    Maninder Jeet Kaur, Ved P. Mishra & Piyush Maheshwari

Authors
  1. Maninder Jeet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ved P. Mishra
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Piyush Maheshwari
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maninder Jeet Kaur .

Editor information

Editors and Affiliations

  1. Through-life Engineering Services Institute, Cranfield University, Cranfield, Bedfordshire, UK

    Maryam Farsi

  2. Faculty of Engineering, Environment and Computing, Coventry University, Coventry, West Midlands, UK

    Alireza Daneshkhah

  3. Department of Business Systems and Operations, University of Northampton, Northampton, Northamptonshire, UK

    Amin Hosseinian-Far

  4. Northumbria University, London, London, Greater London, UK

    Hamid Jahankhani

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kaur, M.J., Mishra, V.P., Maheshwari, P. (2020). The Convergence of Digital Twin, IoT, and Machine Learning: Transforming Data into Action. In: Farsi, M., Daneshkhah, A., Hosseinian-Far, A., Jahankhani, H. (eds) Digital Twin Technologies and Smart Cities. Internet of Things. Springer, Cham. https://doi.org/10.1007/978-3-030-18732-3_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-18732-3_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18731-6

  • Online ISBN: 978-3-030-18732-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation