Skip to main content
SpringerLink
Log in

Cyber-Physical Cloud Computing Systems and Internet of Everything

  • Chapter
  • First Online:
Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 174))

Abstract

The Industry 4.0 is experiencing massive transition in terms of performance and cost efficiency due to the emergence of Disruptive technologies. This applies in particular to smart computing on a big scale such as Cyber Physical Systems (CPS), Cloud Computing, the Internet of Things (IoTs), the Internet of Everything (IoE), Robotics (Mechatronics), Renewable Energy Systems, Autonomous vehicles and Intelligent Cities/Devices. CPS integrates networks, computations and physical processes to control process, respond, give feedback and adapt to changing conditions in the real time. Success of Industry 4.0 is confronted by disruptive CPS difficulties regulated by IoTs and IoE; integration with machine learning functionalities, cloud computing and growing but challenging concentration on the main fields of Big Data Analytics, Virtualization, and Automation. The chapter synthesizes existing literature to highlight drastic alterations that Industry 4.0 will apply on manufacturing systems and processes and explores the various domains revolving around CPS, challenges, applications and the ecosystem. It discusses studies and ways of implementing solutions that have been simplified using standards and systematic methods of investigation.

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. Jóźwiak, L.: Embedded computing technology for highly-demanding cyber-physical systems. IFAC—PapersOnlLine 48(4), 019–030 (2015)

    Article  Google Scholar 

  2. Sztipanovits, J.: 14th Annual IEEE International Conference and Workshops on the Engineering of Computer-Based Systems (ECBS ‘07), pp. 3–6. IEEE Computer Society (2007)

    Google Scholar 

  3. Kramer, B.J.: Evolution of cyber-physical systems: a brief review. In book: Applied Cyber-Physical Systems, Springer (May 2012)

    Google Scholar 

  4. Wang, J., Abid, H., Lee, S., Shu, L., Xia, F.: A secured health care application architecture for cyber-physical systems. Control. Eng. Appl. Inform. 13(3), 101–108 (2011)

    Google Scholar 

  5. Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Futur. Gener. Comput. Syst. 25(6), 599–616 (2009)

    Article  Google Scholar 

  6. Nithya1 S., Sangeetha M., Apinaya Prethi, K.N.: Role of cyber physical systems in health care and survey on security of medical data. Coimbatore Institute of Technology, India

    Google Scholar 

  7. Rawung, R., Putrada, A.: Cyber physical system: paper survey. [online] Available at: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7013187 (2014). Accessed 28 May 2019

  8. Available at: http://chess.eecs.berkeley.edu/cps/

  9. Song, Z., Chen, Y.Q., Sastry, C.R., Tas, N.C.: Optimal Observation for Cyber-Physical Systems: A Fisher-Information-Matrix-Based Approach. Springer-Verlag, London (2009)

    Book  Google Scholar 

  10. Rajkumar, R.: A cyber-physical future. Proc. IEEEE, vol. 100, no. Special Centennial Issue, pp. 1309–1312 (May 2012)

    Google Scholar 

  11. Tricaud, C., Chen, Y.Q.: Optimal mobile actuator/sensor network motion strategy for parameter estimation in a class of cyber physical systems. In: Proceedings of the 2009 American Control Conference St. Louis, MO, 2009, pp. 367–372

    Google Scholar 

  12. Liu, Y., Peng, Y., Wang, B., Yao, S., Liu, Z.: Review on cyber-physical systems. IEEE/CAA J. Autom. Sin., 4(1) (January 2017)

    Article  Google Scholar 

  13. Zhao, W.: Cyber-physical system research. Mar. 2006. [Online]

    Google Scholar 

  14. Available: http://varma.ece.cmu.edu/cps/Presentations/Zhao.pdf

  15. Khaitan, S.K., Mccalley, J.: Design techniques and applications of cyber physical systems: a survey. IEEE Syst. J. (2014)

    Google Scholar 

  16. Chen, H.: Applications of cyber-physical system: a literature review. J. Ind. Integr. Manag. 2(3), 1750012 (28 Pages) (2017)

    Article  Google Scholar 

  17. Kao, Hung-An, Lee, Jay, Siegel, David: A cyber physical interface for automation systems—methodology and examples. J. Mach. 3, 93–106 (2015)

    Article  Google Scholar 

  18. Akhil, J., Aluvalu, R., Samreen, S.: Cyber physical systems for smart cities development. Int. J. Eng. Technol. 7(4.6), 36–38 (2018)

    Google Scholar 

  19. Ghaemi, A.: A cyber phisical system approach to smart city development. In IEEE International Conference on Smart Grids and Cities, pp. 257–262 (2017)

    Google Scholar 

  20. Zanni, A.: Cyber phisical systems and smart cities developer works. IBM (2015)

    Google Scholar 

  21. Broy, M., Cengarle, M.A., et.al.: CPS: Imminent Challenges in Large Scale Complex IT Systems, Development Operations and Management. Springer, pp. 1–28 (2012)

    Google Scholar 

  22. Frmhold-Eisebith M.: Cyber phisical systems in smart cities mastering technological economics and social challenges smart cities, foundations, principles and applications, 1st edn, pp. 1–21. Wiley (2017)

    Google Scholar 

  23. Owen, S., Anil, R.: Ted Dunning, and Ellen Friedman. Mahout in Action. Manning Publications (2011)

    Google Scholar 

  24. Ghoting, A., Krishnamurthy, R., Pednault, E., Reinwald, B., Sindhwani, V., Tatikonda, S., … Vaithyanathan, S.: SystemML: declarative machine learning on MapReduce. In: 2011 IEEE 27th International Conference on Data Engineering (ICDE), pp. 231–242. IEEE (2011)

    Google Scholar 

  25. Bifet, Albert, Holmes, Geoff, Pfahringer, Bernhard, Kranen, Philipp, Kremer, Hardy, Jansen, Timm, Seidl, Thomas: MOA: massive online analysis, a framework for stream classification and clustering. J. Mach. Learn. Res. Proc. Track 11, 44–50 (2010)

    Google Scholar 

  26. Cesa-Bianchi, N., Lugosi, G.: Prediction, learning, and games. Cambridge University Press (2006)

    Google Scholar 

  27. Babcock, B., Babu, S., Datar, M., Motwani, R., & Widom, J. (2002, June). Models and issues in data stream systems. In: Proceedings of the Twenty-first ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (pp. 1–16). ACM

    Google Scholar 

  28. Cormode, G., Muthukrishnan, M.: approximating data with the count-min sketch. Software, IEEE 29(1), 64–69 (2012)

    Article  Google Scholar 

  29. Ntarmos, N., Triantafillou, P., Weikum, G.: Distributed hash sketches: scalable, efficient, and accurate cardinality estimation for distributed multisets. ACM Trans. Comput. Syst. (TOCS) 27(1), 2 (2009)

    Article  Google Scholar 

  30. Chabchoub, Y., & Heébrail, G. (2010, December). Sliding hyperloglog: estimating cardinality in a data stream over a sliding window. In: 2010 IEEE international conference on data mining workshops (ICDMW), (pp. 1297–1303). IEEE

    Google Scholar 

  31. Matusevych, S., Smola, A., Ahmed, A.: Hokusai-sketching streams in real time. arXiv preprint arXiv:1210.4891 (2012)

  32. Heule, S., Nunkesser, M., Hall, A.: HyperLogLog in practice: algorithmic engineering of a state of the art cardinality estimation algorithm (2013)

    Google Scholar 

  33. Chawla, N.V.: Data mining for imbalanced datasets: an overview. In: Data Mining and Knowledge Discovery Handbook (pp. 875–886). Springer US (2010)

    Google Scholar 

  34. Gama, J., Sebastião, R., Rodrigues, P.P.: Issues in evaluation of stream learning algorithms. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 329–338). ACM (2009, June)

    Google Scholar 

  35. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. ACM, Commun (2008)

    Book  Google Scholar 

  36. Apache Haddop: http://hadoop.apache.org/ (2014)

  37. Apache Spark: http://Spark.apache.org/ (2014)

  38. Allam, Z., Dhunny, Z.A.: On big data, artificial intelligence and smart cities. J. Cities, (2018)

    Google Scholar 

  39. Zeid, A., Sundaram, S., Moghaddam, M., Kamarthi, S., Marion, Tucker: Interoperability in smart manufacturing: research challenges. J. Mach. 7, 21 (2019)

    Article  Google Scholar 

  40. Santos, B.P., Santos, F.C., Lima, T.M.: Industry 4.0: an overview. [Available on] https://www.researchgate.net/publication/326352993_Industry_40_an_overview (2018)

  41. Lasi, H., Fettke, P., Kemper, H.G., Feld, T., Hoffmann, M.: Industry 4.0. Bus. & Inf. Syst. Eng., 6(4), 4.0; Bus. & Inf. Syst. Eng., 6(4), 239–242 (2014)

    Google Scholar 

  42. Posada, J., Toro, C., Barandiaran, I., Oyarzun, D., Stricker, D., Amicis, R., Pinto, E.B., Eisert, P., Döllner, J., Vallarino, I.: Visual computing as a key enabling technology for industry 4.0 and industrial internet. IEEE Comput. Graph. Appl. 35(2), 26–40 (2015)

    Article  Google Scholar 

  43. Gizem, E.: How To Define Industry 4.0: Main Pillars Of Industry 4.0. Available at: https://www.researchgate.net/profile/Gizem_Erboz/publication/326557388_How_To_Define_Industry_40_Main_Pillars_Of_Industry_40/links/5b55de5545851507a7c19cc4/How-To-Define-Industry-40-Main-Pillars-Of-Industry-40.pdf?origin=publication_detail (2017). Accessed May 4, 2019

  44. Hofmann, E., Rüsch, M.: Industry 4.0 and the current status as well as future prospects on logistics. Comput. Ind. 89, 23–34 (2017)

    Article  Google Scholar 

  45. Gartner—IT Glossary. Available at: https://www.gartner.com/it-glossary/digitalization/. Accessed April 21, 2019

  46. Gray, J., Rumpe, B.: Models for Digitalization. Softw. Syst. Model. 14(4), 1319–1320 (2015). https://doi.org/10.1007/s10270-015-0494-9

    Article  Google Scholar 

  47. Scardapane, S., Wang, D., Panella, M.: A decentralized training algorithm for echo state networks in distributed big data applications. Neural Netw. 78, 65–74 (2016)

    Article  Google Scholar 

  48. Ungurean, I., Gaitan, V.G.: An IoT architecture for things from industrial environment. In: Communications (COMM), IEEE 2014 10th International Conference, pp. 1–4 (2014)

    Google Scholar 

  49. Lu, Y.: Industry 4.0: a survey on technologies, applications and open research issues. J. Ind. Inf. Integr. 6, 1–10 (2017)

    Google Scholar 

  50. Lin, F., Chen, C., Zhang, N., Guan, X., Shen, X.: Autonomous channel switching: towards efficient spectrum sharing for industrial wireless sensor networks. IEEE Internet Things J. 3(2), 231–243 (2016)

    Article  Google Scholar 

  51. Vijaykumar, S., Saravanakumar, S.G., Balamurugan, M.: Unique sense: smart computing prototype for industry 4.0 revolution with IOT and bigdata implementation model. Indian J. Sci. Technol. 8(5), 1–4 (2015)

    Google Scholar 

  52. Geographica.: Trends in digital transformation in the retail sector. Available at: https://geographica.com/en/blog/retail-sector/ (2019) Accessed April 24, 2019

  53. Rahman, H., Rahmani, R.: Enabling distributed intelligence assisted future internet of things controller (FITC). Applied Computing and Informatics. Available at: http://linkinghub.elsevier.com/retrieve/pii/S2210832717300364 (2017). Accessed May 4, 2019

  54. Thames, L., Schaefer, D.: Software-defined cloud manufacturing for Industry 4.0. Procedia CIRP 52, 12–17 (2016)

    Article  Google Scholar 

  55. Conti, M., Das, S., Bisdikian, C., Kumar, M., Ni, L., Passarella, A., Roussos, G., Tröster, G., Tsudik, G., Zambonelli, F.: Looking ahead in pervasive computing: Challenges and opportunities in the era of cyber–physical convergence. Pervasive Mob. Computing. Val. 8, 2–21 (2012)

    Article  Google Scholar 

  56. Lee, E.: Computing needs time. Commun. ACM 52(5), 70–79 (2009)

    Article  Google Scholar 

  57. National Science Foundation: Cyber Physical Systems, Program Solicitation. NSF 10–515 Available at: https://www.nsf.gov/pubs/2010/nsf10515/nsf10515.htm. Accessed May 4 2019

  58. Ivanov, D., Sokolov, B., Ivanova, M.: Schedule coordination in cyber-physical supply networks Industry 4.0, IFAC-PapersOnLine Vol.49, 12, 839–844 (2016)

    Article  Google Scholar 

  59. Posada, J., Toro, C., Barandiaran, I., Oyarzun, D., Stricker, D. Amicis, R., Vallarino, I.:Visual computing as a key enabling technology for Industry 4.0 and industrial internet. IEEE Comput. Graphics Appl. 35(2), 26–40 (2015)

    Google Scholar 

  60. Roblek, V., Meško, M., and Krapež, A.: A complex view of Industry 4.0, SAGE Open 6(2) (2016)

    Article  Google Scholar 

  61. Shafiq, S.I., Sanin, C., Toro, C., Szczerbicki, E.: Virtual engineering object (VEO): toward experience-based design and manufacturing for Industry 4.0, Cybern. Syst. 46(1–2), 35–50 (2015)

    Article  Google Scholar 

  62. Shafiq, S.I., Sanin, C., Szczerbicki, E., Toro, C.: Virtual engineering factory: creating experience base for Industry 4.0, Cybern. Syst. 47(1–2), 32–47 (2016)

    Article  Google Scholar 

  63. Berre, A.J., Elvesæter, B., Figay, N., Guglielmina, C., Johnsen, S.G., Karlsen, D., Lippe, S.: The ATHENA interoperability framework. Enterprise Interoperability II, pp. 569–580. Springer, London (2007)

    Chapter  Google Scholar 

  64. Ruggaber, R.: Athena-advanced technologies for interoperability of heterogeneous enterprise networks and their applications. Interoperability Enterp. Software Appl. SAP Research, pp. 459–460 (2006)

    Google Scholar 

  65. Sowell, P.K.: The C4ISR architecture framework: history, status, and plans for evolution. Mitre Corp, Mclean, VA (2006)

    Book  Google Scholar 

  66. Synergy, European interoperability framework v 1.0, The IDABC Q. (2005) 01 (January), 2005

    Google Scholar 

  67. Science and Technology Options Assessment (STOA): Annual report for 2015, http://www.europarl.europa.eu/RegData/etudes/STUD/2016/563507/EPRS_STU(2016)563507_EN.pdf

  68. https://ec.europa.eu/digital-single-market/en/news/new-report-shows-digital-skills-are-required-all-types-jobs

  69. https://www.pzh.uni-hannover.de/fileadmin/PZH/_downloads/2016/WhatisthePZH_160219_2.pdf

  70. https://ec.europa.eu/growth/tools-databases/regional-innovation-monitor/organisation/hannover-centre-production-technology-pzh

Download references

Author information

Authors and Affiliations

  1. Amity University Dubai International Academic City, Dubai, UAE

    Maninder Jeet Kaur

  2. SP Jain School of Global Management, Dubai International Academic City, Dubai, UAE

    Sadia Riaz

  3. City University College of Ajman, Dubai, UAE

    Arif Mushtaq

Authors
  1. Maninder Jeet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sadia Riaz
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Arif Mushtaq
    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. CSIE Department, National Dong Hwa University, New Taipei City, Taiwan

    Sheng-Lung Peng

  2. Department of Computer Science and Engineering, Brainware University, Kolkata, West Bengal, India

    Souvik Pal

  3. Department of Communications Engineering, Xiamen University, Xiamen City, Fujian, China

    Lianfen Huang

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., Riaz, S., Mushtaq, A. (2020). Cyber-Physical Cloud Computing Systems and Internet of Everything. In: Peng, SL., Pal, S., Huang, L. (eds) Principles of Internet of Things (IoT) Ecosystem: Insight Paradigm. Intelligent Systems Reference Library, vol 174. Springer, Cham. https://doi.org/10.1007/978-3-030-33596-0_8

Download citation

Publish with us

Policies and ethics

Search

Navigation