Skip to main content
SpringerLink
Log in

A Review and Analysis of the Characteristics of Cyber-physical Systems in Industry 4.0

  • Original Research
  • Published:
SN Computer Science Aims and scope Submit manuscript

Abstract

Industry 4.0 (I4.0) creates more efficient production processes by providing an interconnected environment between man and machine. Cyber-physical systems (CPS) are one of the many technologies that enable I4.0 by building a bridge between the physical and the virtual objects in production systems. Nonetheless, CPSs are dealing with a complex system with various emergent behaviours. CPS must be defined by features and characteristics that can adapt to the changes in real-time and derive knowledge through the gathered abundant information it receives. In this respect, this study focuses on an analysis and a review of CPS and its characteristics to explore the essence of knowledge representation in CPS metamodels. This study aims to answer the following research questions: how are CPS metamodels described and characterized? How is Knowledge represented in CPS metamodels? To respond to the research questions and achieve the purpose of this study, first a literature review was conducted to identify relevant papers, then Formal Concept Analysis (FCA) as a clustering technique is used to make a more thorough investigation of the topic, to analyse CPS characteristics, and to discover any hidden relationship between them. The analysis conducted led to an understanding of CPS’s characteristics and the discovery of any hidden relationship among them. Among all characteristics (e.g., safety, fault-tolerant, redundancy), “resiliency” was the most frequent characteristic. Consequently, with the help of the hidden bonds found by FCA among the most frequent and the most observed characteristics, a hierarchy of highly ranked CPS characteristics as a road map to reach “resiliency” is proposed. The paper presented a review and an analysis of Cyber-physical systems and their representative characteristics. A new set of definitions for the highly ranked characteristics is also introduced. The proposed definitions can help the future CPS metamodel designs so that they take a path more aligned with the concept of Industry 4.0.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The data related to the study are all present in the article. No excess data ia available to share.

References

  1. Eslami Y, Ashouri S, Franciosi C, Lezoche M. Knowledge extraction in cyber-physical systems meta-models: A formal concept analysis application. In: Proceedings of the 3rd International Conference on Innovative Intelligent Industrial Production and Logistics - IN4PL; ISBN 978-989-758-612-5; ISSN 2184-9285. SciTePress;2022. pp. 129-136. https://doi.org/10.5220/0011536700003329.

  2. Liu Y, Peng Y, Wang B, Yao S, Liu Z. Review on cyber-physical systems. IEEE/CAA J Autom Sin. 2017;4:27–40. https://doi.org/10.1109/JAS.2017.7510349.

    Article  Google Scholar 

  3. Someswara Rao C, Shiva Shankar R, Murthy KVS (2020) Cyber-Physical System—An Overview. In: Satapathy S, Bhateja V, Mohanty J, Udgata S (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 160. Singapore: Springer. https://doi.org/10.1007/978-981-32-9690-9_54.

  4. Verma R. Smart city healthcare cyber physical system: characteristics, technologies and challenges. Wirel Pers Commun. 2022;122:1413–33. https://doi.org/10.1007/s11277-021-08955-6.

    Article  Google Scholar 

  5. Wu X, Goepp V, Siadat A. Cyber physical production systems: a review of design and implementation approaches. 2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Macao, China, 2019. pp. 1588-1592. https://doi.org/10.1109/IEEM44572.2019.8978654.

  6. Juhlin P, Schlake JC, Janka D, Hawlitschek A. Metamodeling of Cyber-Physical Production Systems using AutomationML for Collaborative Innovation. 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Vasteras, Sweden, 2021. pp. 1-4. https://doi.org/10.1109/ETFA45728.2021.9613560.

  7. Chen H. Applications of cyber-physical system: a literature review. J Ind Integr Manag. 2017. https://doi.org/10.1142/S2424862217500129.

    Article  Google Scholar 

  8. Mehdipour F. Smart field monitoring: an application of cyber-physical systems in agriculture (work in progress). Proceedings—2014 IIAI 3rd international conference on advanced applied informatics, IIAI-AAI 2014. 2014. p. 181–84. https://doi.org/10.1109/IIAI-AAI.2014.46.

  9. Wan J, Yan H, Li D, Zhou K, Zeng L. Cyber-physical systems for optimal energy management scheme of autonomous electric vehicle. Comput J. 2013;56:947–56. https://doi.org/10.1093/comjnl/bxt043.

    Article  Google Scholar 

  10. Medhat R, Bonakdarpour B, Kumar D, Fischmeister S. Runtime monitoring of cyber-physical systems under timing and memory constraints. ACM Trans Embed Comput Syst. 2015;14:79:1-79:29. https://doi.org/10.1145/2744196.

    Article  Google Scholar 

  11. Basile F, Chiacchio P, Coppola J, Gerbasio D. Automated warehouse systems: a cyber-physical system perspective. In: 2015 IEEE 20th conference on emerging technologies factory automation (ETFA). 2015. p. 1–4. https://doi.org/10.1109/ETFA.2015.7301597.

  12. Zhou Y, Xiao Q, Mo Z, Chen S, Yin Y. Privacy-preserving point-to-point transportation traffic measurement through bit array masking in intelligent cyber-physical road systems. 2013. p. 826–33. https://doi.org/10.1109/GreenCom-iThings-CPSCom.2013.146.

  13. Zhou Y, Mo Z, Xiao Q, Chen S, Yin Y. Privacy-preserving transportation traffic measurement in intelligent cyber-physical road systems. IEEE Trans Veh Technol. 2015;65:1–1. https://doi.org/10.1109/TVT.2015.2436395.

    Article  Google Scholar 

  14. Wille R. Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts. In: Rival I, editor. Ordered Sets. NATO Advanced Study Institutes Series, vol 83. Springer; Dordrecht: 1982. https://doi.org/10.1007/978-94-009-7798-3_15.

  15. Hu Q, Yuan Z, Qin K, Zhang J. A novel outlier detection approach based on formal concept analysis. Knowl Based Syst. 2023;268: 110486. https://doi.org/10.1016/j.knosys.2023.110486.

    Article  Google Scholar 

  16. Mezni H, Sellami M. Multi-cloud service composition using formal concept analysis. J Syst Softw. 2017;134:138–52. https://doi.org/10.1016/j.jss.2017.08.016.

    Article  Google Scholar 

  17. Wajnberg M, Lezoche M, Massé BA, Valtchev P, Panetto H. Complex system tacit knowledge extraction trough a formal method. INSIGHT Int Counc Syst Eng (INCOSE). 2017;20:23–6. https://doi.org/10.1002/inst.12176.

    Article  Google Scholar 

  18. Kim E-H, Kim H-G, Hwang S-H, Lee S-I. FARM: an FCA-based association rule miner. Knowl Based Syst. 2015;85:277–97. https://doi.org/10.1016/j.knosys.2015.05.013.

    Article  Google Scholar 

  19. Hornik K, Grün B, Hahsler M. arules—a computational environment for mining association rules and frequent item sets. J Stat Softw. 2005. https://doi.org/10.18637/jss.v014.i15.

    Article  Google Scholar 

  20. Griffor ER, Greer C, Wollman DA, Burns MJ. Framework for cyber-physical systems: volume 1, overview. Gaithersburg: National Institute of Standards and Technology; 2017. https://doi.org/10.6028/NIST.SP.1500-201.

    Book  Google Scholar 

  21. Napoleone A, Macchi M, Pozzetti A. A review on the characteristics of cyber-physical systems for the future smart factories. J Manuf Syst. 2020;54:305–35. https://doi.org/10.1016/j.jmsy.2020.01.007.

    Article  Google Scholar 

  22. Lezoche M, Panetto H. Cyber-physical systems, a new formal paradigm to model redundancy and resiliency. Enterp Inf Syst. 2018. https://doi.org/10.1080/17517575.2018.1536807.

    Article  Google Scholar 

  23. Ilsen R, Meissner H, Aurich JC. Optimizing energy consumption in a decentralized manufacturing system. J Comput Inf Sci Eng. 2017. https://doi.org/10.1115/1.4034585.

    Article  Google Scholar 

  24. Yuan X, Anumba CJ, Parfitt KM. Review of the potential for a cyber-physical system approach to temporary structures monitoring. Int J Archit Res. 2015. https://doi.org/10.26687/archnet-ijar.v9i3.841.

    Article  Google Scholar 

  25. Rosenberg EH. Smart architecture-bots & Industry 4.0 principles for architecture. In: Martens B, Wurzer G, Grasl T, Lorenz WE, Schaffranek R, editors. Real time—proceedings of the 33rd ECAADe conference—volume 2. Vienna: Vienna University of Technology, 16–18 September 2015. CUMINCAD, 2015. p. 251–59. http://papers.cumincad.org/cgi-bin/works/BasketShow&editable=1/Show?ecaade2015_155. Accessed 25 Apr 2020.

  26. Ghobakhloo M. The future of manufacturing industry: a strategic roadmap toward Industry 4.0. J Manuf Technol Manag. 2018;29:910–36. https://doi.org/10.1108/JMTM-02-2018-0057.

    Article  Google Scholar 

  27. Upasani K, Bakshi M, Pandhare V, Lad BK. Distributed maintenance planning in manufacturing industries. Comput Ind Eng. 2017;108:1–14. https://doi.org/10.1016/j.cie.2017.03.027.

    Article  Google Scholar 

  28. Tu M, Lim MK, Yang M-F. IoT-based production logistics and supply chain system—part 2: IoT-based cyber-physical system: a framework and evaluation. Ind Manag Data Syst. 2018;118:96–125. https://doi.org/10.1108/IMDS-11-2016-0504.

    Article  Google Scholar 

  29. Rajkumar R, Lee I, Sha L, Stankovic J. Cyber-physical systems: the next computing revolution. In: Proceedings of the 47th design automation conference, association for computing machinery. Anaheim, California; 2010. p. 731–36. https://doi.org/10.1145/1837274.1837461.

  30. Wu F-J, Kao Y-F, Tseng Y-C. From wireless sensor networks towards cyber physical systems. Pervasive Mob Comput. 2011;7:397–413. https://doi.org/10.1016/j.pmcj.2011.03.003.

    Article  Google Scholar 

  31. Wang L, Haghighi A. Combined strength of holons, agents and function blocks in cyber-physical systems. J Manuf Syst. 2016;40:25–34. https://doi.org/10.1016/j.jmsy.2016.05.002.

    Article  Google Scholar 

  32. Lee H, Ryu K, Cho Y. A framework of a smart injection molding system based on real-time data. Procedia Manuf. 2017;11:1004–11. https://doi.org/10.1016/j.promfg.2017.07.206.

    Article  Google Scholar 

  33. Fettermann DC, Cavalcante CGS, de Almeida TD, Tortorella GL. How does Industry 4.0 contribute to operations management? J Ind Prod Eng. 2018;35:255–68. https://doi.org/10.1080/21681015.2018.1462863.

    Article  Google Scholar 

  34. Chen B, Wan J, Shu L, Li P, Mukherjee M, Yin B. Smart factory of industry 4.0: key technologies, application case, and challenges. IEEE Access. 2018;6:6505–19. https://doi.org/10.1109/ACCESS.2017.2783682.

    Article  Google Scholar 

  35. Lee J, Jin C, Bagheri B. Cyber physical systems for predictive production systems. Prod Eng Res Dev. 2017;11:155–65. https://doi.org/10.1007/s11740-017-0729-4.

    Article  Google Scholar 

  36. Scholze S, Barata J, Stokic D. Holistic context-sensitivity for run-time optimization of flexible manufacturing systems. Sensors. 2017;17:455. https://doi.org/10.3390/s17030455.

    Article  Google Scholar 

  37. Leitão P, Barbosa J, Papadopoulou M-E, Venieris IS. Standardization in cyber-physical systems: the ARUM case, in. IEEE Int Conf Ind Technol (ICIT). 2015;2015:2988–93. https://doi.org/10.1109/ICIT.2015.7125539.

    Article  Google Scholar 

  38. Yu Z, Ouyang J, Li S, Peng X. Formal modeling and control of cyber-physical manufacturing systems. Adv Mech Eng. 2017. https://doi.org/10.1177/1687814017725472.

    Article  Google Scholar 

  39. Schuh G, Gartzen T, Rodenhauser T, Marks A. Promoting Work-based Learning through INDUSTRY 4.0. Procedia CIRP. 2015;32:82–7. https://doi.org/10.1016/j.procir.2015.02.213.

    Article  Google Scholar 

  40. Heiss M, Oertl A, Sturm M, Palensky P, Vielguth S, Nadler F. Platforms for industrial cyber-physical systems integration: contradicting requirements as drivers for innovation. In: 2015 Workshop on modeling and simulation of cyber-physical energy systems (MSCPES). 2015. p. 1–8. https://doi.org/10.1109/MSCPES.2015.7115405.

  41. Wang L, Törngren M, Onori M. Current status and advancement of cyber-physical systems in manufacturing. J Manuf Syst. 2015;37:517–27. https://doi.org/10.1016/j.jmsy.2015.04.008.

    Article  Google Scholar 

  42. Lee EA. Cyber-physical systems-are computing foundations adequate, position paper for NSF workshop on cyber-physical systems: research motivation, techniques and roadmap. 2006.

  43. Mourtzis D, Vlachou E. Cloud-based cyber-physical systems and quality of services. TQM J. 2016;28:704–33. https://doi.org/10.1108/TQM-10-2015-0133.

    Article  Google Scholar 

  44. Hofmann E, Rüsch M. Industry 4.0 and the current status as well as future prospects on logistics. Comput Ind. 2017;89:23–34. https://doi.org/10.1016/j.compind.2017.04.002.

    Article  Google Scholar 

  45. Zhang H, Peng C, Sun H, Du D. Adaptive state estimation for cyber physical systems under sparse attacks. Trans Inst Meas Control. 2019;41:1571–9. https://doi.org/10.1177/0142331217730123.

    Article  Google Scholar 

  46. Mora H, Colom JF, Gil D, Jimeno-Morenilla A. Distributed computational model for shared processing on cyber-physical system environments. Comput Commun. 2017;111:68–83. https://doi.org/10.1016/j.comcom.2017.07.009.

    Article  Google Scholar 

  47. Etxeberria-Agiriano I, Calvo I, Noguero A, Zulueta E. Configurable cooperative middleware for the next generation of CPS. In: 2012 9th international conference on remote engineering and virtual instrumentation (REV). 2012. p. 1–5. https://doi.org/10.1109/REV.2012.6293154.

  48. Morgan J, O’Donnell GE. Enabling a ubiquitous and cloud manufacturing foundation with field-level service-oriented architecture. Int J Comput Integr Manuf. 2017;30:442–58. https://doi.org/10.1080/0951192X.2015.1032355.

    Article  Google Scholar 

  49. Otto J, Vogel-Heuser B, Niggemann O. Automatic parameter estimation for reusable software components of modular and reconfigurable cyber-physical production systems in the domain of discrete manufacturing. IEEE Trans Ind Inf. 2018;14:275–82. https://doi.org/10.1109/TII.2017.2718729.

    Article  Google Scholar 

  50. Ribeiro L, Hochwallner M. On the design complexity of cyberphysical production systems. Complexity. 2018;2018: e4632195. https://doi.org/10.1155/2018/4632195.

    Article  Google Scholar 

  51. 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. 2018;12:3816–27. https://doi.org/10.1109/JSYST.2017.2771139.

    Article  Google Scholar 

  52. Basu S. Plant hazard analysis and safety instrumentation systems. Cambridge: Academic Press; 2016.

    Google Scholar 

  53. Gujrati S, Zhu H, Singh G. Composable algorithms for interdependent cyber physical systems. In: 2015 resilience week (RWS). 2015. p. 1–6. https://doi.org/10.1109/RWEEK.2015.7287431.

  54. Lazarova-Molnar S, Mohamed N, Shaker HR. Reliability modeling of cyber-physical systems: a holistic overview and challenges. In: 2017 workshop on modeling and simulation of cyber-physical energy systems (MSCPES). 2017. p. 1–6. https://doi.org/10.1109/MSCPES.2017.8064536.

  55. Sabaliauskaite G, Mathur AP. Aligning cyber-physical system safety and security. In: Cardin M-A, Krob D, Lui PC, Tan YH, Wood K, editors. Complex systems design and management Asia. Cham: Springer International Publishing; 2015. p. 41–53. https://doi.org/10.1007/978-3-319-12544-2_4.

    Chapter  Google Scholar 

  56. Mahmoud MS, Xia Y. Networked control systems: cloud control and secure control. New York: Butterworth-Heinemann; 2019.

    Book  Google Scholar 

  57. Morozov D, Lezoche M, Panetto H. Multi-paradigm modelling of cyber-physical systems. IFAC PapersOnLine. 2018;51:1385–90. https://doi.org/10.1016/j.ifacol.2018.08.334.

    Article  Google Scholar 

  58. Cobos Méndez R, de Oliveira Filho J, Dresscher D, Broenink J. A bond-graph metamodel:: physics-based interconnection of software components, lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). 12018 LNCS. 2020. p. 87–105. https://doi.org/10.1007/978-3-030-40914-2_5.

  59. Jeon J, Chun I, Kim W. Metamodel-based CPS modeling tool. In: Park JH, Jeong Y-S, Park SO, Chen H-C, editors. Embedded and Multimedia computing technology and service. Dordrecht: Springer; 2012. p. 285–91. https://doi.org/10.1007/978-94-007-5076-0_33.

    Chapter  Google Scholar 

  60. Mezhuyev V, Samet R. Geometrical meta-metamodel for cyber-physical modelling. In: 2013 international conference on cyberworlds. 2013. p. 89–93. https://doi.org/10.1109/CW.2013.14.

  61. Tavčar J, Duhovnik J, Horváth I. From Validation of medical devices towards validation of adaptive cyber-physical systems. J Integr Des Process Sci. 2020;23:37–59. https://doi.org/10.3233/JID190008.

    Article  Google Scholar 

  62. Cossentino M, Lopes S, Renda G, Sabatucci L, Zaffora F. A Metamodel of a Multi-Paradigm Approach to Smart Cyber-Physical Systems Development. In WOA. 2019. pp. 35–41.

  63. Yilma BA, Panetto H, Naudet Y. A meta-model of cyber-physical-social system: the CPSS paradigm to support human-machine collaboration in industry 4.0. IFIP Adv Inf Commun Technol. 2019;568:11–20. https://doi.org/10.1007/978-3-030-28464-0_2.

    Article  Google Scholar 

  64. Baar T. A metamodel-based approach for adding modularization to KeYmaera’s input syntax, lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). 11964 LNCS. 2019. p. 125–139. https://doi.org/10.1007/978-3-030-37487-7_11.

  65. Fatehah M, Mezhuyev V. Design and process metamodels for modelling and verification of safety-related software applications in smart building systems. ICIT 2018: Proceedings of the 6th International Conference on Information Technology: IoT and Smart City. 2018. pp. 60-64. https://doi.org/10.1145/3301551.3301577.

  66. Alrimawi F, Pasquale L, Mehta D, Nuseibeh B. I've seen this before: Sharing cyber-physical incident knowledge. 2018 IEEE/ACM 1st International Workshop on Security Awareness from Design to Deployment (SEAD). Gothenburg, Sweden: 2018. pp. 33-40. https://doi.org/10.1145/3194707.3194714.

  67. Merschak S, Hehenberger P, Witters M, Gadeyne K. A hierarchical meta-model for the design of cyber-physical production systems. 2018 19th International Conference on Research and Education in Mechatronics (REM). Delft, Netherlands: 2018. pp. 36-41. https://doi.org/10.1109/REM.2018.8421784.

  68. Huang P, Jiang K, Guan C, Du D. Towards modeling cyber-physical systems with SysML/MARTE/pCCSL. 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC). Tokyo, Japan: 2018. pp. 264-269. https://doi.org/10.1109/COMPSAC.2018.00042.

  69. Zhang M, Selic B, Ali S, Yue T, Okariz O, Norgren R. Understanding uncertainty in cyber-physical systems: a conceptual model, lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics, 9764. 2016; p. 247–64. https://doi.org/10.1007/978-3-319-42061-5_16.

  70. Athinaiou M, Mouratidis H, Fotis T, Pavlidis M, Panaousis E. Towards the definition of a security incident response modelling language, lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). 11033 LNCS. 2018. p. 198–212. https://doi.org/10.1007/978-3-319-98385-1_14.

  71. Zhao Y, Rao Y. A CPS-based intelligence-awareness platform for IT service management. 2017. p. 6668–73. https://doi.org/10.1109/CAC.2017.8243978.

  72. Carmen Cheh, Ken Keefe, Brett Feddersen, Binbin Chen, William G. Temple, and William H. Sanders. 2017. Developing Models for Physical Attacks in Cyber-Physical Systems. In Proceedings of the 2017 Workshop on Cyber-Physical Systems Security and PrivaCy (CPS '17). Association for Computing Machinery, New York, NY, USA, 49–55. https://doi.org/10.1145/3140241.3140249.

  73. M. Tuo, X. Zhou, G. Yang and N. Fu, "An Approach for Safety Analysis of Cyber-Physical System Based on Model Transformation," 2016 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Chengdu, China, 2016, pp. 636-639. https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData.2016.140.

  74. Islam N, Azim A. Feature characterization for CPS software reuse. In: Proceedings of the 10th ACM/IEEE international conference on cyber-physical systems, association for computing machinery. 2019; Montreal. p. 314–15. https://doi.org/10.1145/3302509.3313318.

  75. J. Liu and L. Zhang, "QoS Modeling for Cyber-Physical Systems Using Aspect-Oriented Approach," 2011 Second International Conference on Networking and Distributed Computing, Beijing, China, 2011, pp. 154-158. https://doi.org/10.1109/ICNDC.2011.38.

  76. Iglesias-Urkia M, Iglesias A, López-Davalillo B, Charramendieta S, Casado-Mansilla D, Sagardui G, Urbieta A. TRILATERAL: a model-based approach for industrial CPS—monitoring and control. Communications in computer and information science. 1161 CCIS. 2020. p. 376–398. https://doi.org/10.1007/978-3-030-37873-8_16.

  77. Matt Bunting and Jonathan Sprinkle. 2019. A meta-metamodel for dynamic constraint feedback in modeling languages. In Proceedings of the 17th ACM SIGPLAN International Workshop on Domain-Specific Modeling (DSM 2019). Association for Computing Machinery, New York, NY, USA, 11–19. https://doi.org/10.1145/3358501.3361239.

  78. Smarsly, K., Fitz, T., & Legatiuk, D. (2018). Metamodeling Wireless Communication in Cyber-Physical Systems. In EG-ICE.

  79. Zhang H, Liu J, Kato N. Threshold tuning-based wearable sensor fault detection for reliable medical monitoring using Bayesian network model. IEEE Syst J. 2018;12:1886–96. https://doi.org/10.1109/JSYST.2016.2600582.

    Article  Google Scholar 

  80. M. Walch, "Knowledge-driven enrichment of cyber-physical systems for industrial applications using the KbR modelling approach," 2017 IEEE International Conference on Agents (ICA), Beijing, China, 2017, pp. 84-89. https://doi.org/10.1109/AGENTS.2017.8015307.

  81. Smarsly K, Theiler M, Dragos K. IFC-based modeling of cyber-physical systems in civil engineering. 2017. p. 269–78.

  82. Wang J, Abid H, Lee S, Shu L, Xia F. A secured health care application architecture for cyber-physical systems. arXiv:1201.0213 [Cs]. 2011. http://arxiv.org/abs/1201.0213. Accessed 19 May 2020.

  83. Cao X, Cheng P, Chen J, Sun Y. An online optimization approach for control and communication codesign in networked cyber-physical systems. IEEE Trans Ind Inf. 2013;9:439–50. https://doi.org/10.1109/TII.2012.2216537.

    Article  Google Scholar 

  84. Sampigethaya K, Poovendran R. Aviation cyber-physical systems: foundations for future aircraft and air transport. Proc IEEE. 2013;101:1834–55. https://doi.org/10.1109/JPROC.2012.2235131.

    Article  Google Scholar 

  85. Banerjee A, Kandula S, Mukherjee T, Gupta SKS. BAND-AiDe: a tool for cyber-physical oriented analysis and design of body area networks and devices. ACM Trans Embed Comput Syst. 2012;11:49:1-49:29. https://doi.org/10.1145/2331147.2331159.

    Article  Google Scholar 

  86. Xiong G, Zhu F, Liu X, Dong X, Huang W, Chen S, Zhao K. Cyber-physical-social system in intelligent transportation. IEEE/CAA J Autom Sin. 2015;2:320–33. https://doi.org/10.1109/JAS.2015.7152667.

    Article  MathSciNet  Google Scholar 

  87. Wan J, Chen M, Xia F, Di L, Zhou K. From machine-to-machine communications towards cyber-physical systems. Comput Sci Inf Syst. 2013;10:1105–28.

    Article  Google Scholar 

  88. Leitão P, Colombo AW, Karnouskos S. Industrial automation based on cyber-physical systems technologies: prototype implementations and challenges. Comput Ind. 2016;81:11–25. https://doi.org/10.1016/j.compind.2015.08.004.

    Article  Google Scholar 

  89. Eyisi E, Zhang Z, Koutsoukos X, Porter J, Karsai G, Sztipanovits J. Model-based control design and integration of cyberphysical systems: an adaptive cruise control case study. J Control Sci Eng. 2013;2013: e678016. https://doi.org/10.1155/2013/678016.

    Article  MATH  Google Scholar 

  90. Lai C-F, Ma Y-W, Chang S-Y, Chao H-C, Huang Y-M. OSGi-based services architecture for cyber-physical home control systems. Comput Commun. 2011;34:184–91. https://doi.org/10.1016/j.comcom.2010.03.034.

    Article  Google Scholar 

  91. Sangiovanni-Vincentelli A, Damm W, Passerone R. Taming Dr. Frankenstein: contract-based design for cyber-physical systems. Eur J Control. 2012;18:217–38. https://doi.org/10.3166/ejc.18.217-238.

    Article  MathSciNet  MATH  Google Scholar 

  92. Sztipanovits J, Koutsoukos X, Karsai G, Kottenstette N, Antsaklis P, Gupta V, Goodwine B, Baras J, Wang S. Toward a science of cyber-physical system integration. Proc IEEE. 2012;100:29–44. https://doi.org/10.1109/JPROC.2011.2161529.

    Article  Google Scholar 

  93. Sampath Kumar VR, Shanmugavel M, Ganapathy V, Shirinzadeh B. Unified meta-modeling framework using bond graph grammars for conceptual modelling. Robot Auton Syst. 2015;72:114–30. https://doi.org/10.1016/j.robot.2015.05.003.

    Article  Google Scholar 

  94. Bagheri B, Yang S, Kao H-A, Lee J. Cyber-physical systems architecture for self-aware machines in industry 4.0 environment. IFAC PapersOnLine. 2015;28:1622–7. https://doi.org/10.1016/j.ifacol.2015.06.318.

    Article  Google Scholar 

  95. Dillon TS, Zhuge H, Wu C, Singh J, Chang E. Web-of-things framework for cyber-physical systems. Concurr Comput Pract Exp. 2011;23:905–23. https://doi.org/10.1002/cpe.1629.

    Article  Google Scholar 

  96. Hu F, Lu Y, Vasilakos AV, Hao Q, Ma R, Patil Y, Zhang T, Lu J, Li X, Xiong NN. Robust cyber-physical systems: concept, models, and implementation. Futur Gener Comput Syst. 2016;56:449–75. https://doi.org/10.1016/j.future.2015.06.006.

    Article  Google Scholar 

  97. Luis E. Salazar and Alvaro A. Cardenas. 2019. Enhancing the Resiliency of Cyber-Physical Systems with Software-Defined Networks. In Proceedings of the ACM Workshop on Cyber-Physical Systems Security & Privacy (CPS-SPC'19). Association for Computing Machinery, New York, NY, USA, 15–26. https://doi.org/10.1145/3338499.3357356.

  98. P. Buason, H. Choi, A. Valdes and H. J. Liu, "Cyber-Physical Systems of Microgrids for Electrical Grid Resiliency," 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS), Taipei, Taiwan, 2019, pp. 492-497. https://doi.org/10.1109/ICPHYS.2019.8780336.

  99. Potteiger B, Zhang Z, Koutsoukos X. Integrated moving target defense and control reconfiguration for securing cyber-physical systems. Microprocess Microsyst. 2020. https://doi.org/10.1016/j.micpro.2019.102954.

    Article  Google Scholar 

  100. Zhang M, Ali S, Yue T, Norgren R, Okariz O. Uncertainty-wise cyber-physical system test modeling. Softw Syst Model. 2019;18:1379–418. https://doi.org/10.1007/s10270-017-0609-6.

    Article  Google Scholar 

  101. A. Bin Masood, H. K. Qureshi, S. M. Danish and M. Lestas, "Realizing an Implementation Platform for Closed Loop Cyber-Physical Systems Using Blockchain," 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), Kuala Lumpur, Malaysia, 2019, pp. 1-5. https://doi.org/10.1109/VTCSpring.2019.8746372.

  102. Mussard-Afcari Y, Rawat DB, Garuba M. Y. Mussard-Afcari, D. B. Rawat and M. Garuba, "Data Validation and Correction for Resiliency in Mobile Cyber-Physical Systems," 2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA, 2019, pp. 1-4. https://doi.org/10.1109/CCNC.2019.8651853.

  103. Sierla S, O’Halloran BM, Karhela T, Papakonstantinou N, Tumer IY. Common cause failure analysis of cyber-physical systems situated in constructed environments. Res Eng Des. 2013;24:375–94. https://doi.org/10.1007/s00163-013-0156-2.

    Article  Google Scholar 

  104. Wan K, Alagar V. Context-aware security solutions for cyber-physical systems. Mob Netw Appl. 2014;19:212–26. https://doi.org/10.1007/s11036-014-0495-x.

    Article  Google Scholar 

  105. Kantarci B. Cyber-physical alternate route recommendation system for paramedics in an urban area. In: 2015 IEEE wireless communications and networking conference (WCNC). 2015. p. 2155–2160. https://doi.org/10.1109/WCNC.2015.7127801.

  106. Wiesner S, Marilungo E, Thoben K-D. Cyber-physical product-service systems—challenges for requirements engineering. Int J Autom Technol. 2017;11:17–28. https://doi.org/10.20965/ijat.2017.p0017.

    Article  Google Scholar 

  107. Mo Y, Sinopoli B. Integrity attacks on cyber-physical systems. In: Proceedings of the 1st international conference on high confidence networked systems, association for computing machinery. Beijing, China; 2012. p. 47–54. https://doi.org/10.1145/2185505.2185514.

  108. Burmester M, Magkos E, Chrissikopoulos V. Modeling security in cyber–physical systems. Int J Crit Infrastruct Prot. 2012;5:118–26. https://doi.org/10.1016/j.ijcip.2012.08.002.

    Article  Google Scholar 

  109. Smart Systems and Cyber Physical Systems paradigms in an IoT and Industry/ie4.0 context, (n.d.). https://scholar.googleusercontent.com/scholar?q=cache:8NNXnBZaMc8J:scholar.google.com/+Smart+Systems+and+Cyber+Physical+Systems+paradigms+in+an+IoT+and+Industry/ie4.+0+context&hl=en&as_sdt=0,5. Accessed 30 June 2020.

  110. Boyes HA. Trustworthy cyber-physical systems—a review (2013) 31. https://doi.org/10.1049/cp.2013.1707.

  111. Ratasich D, Platzer M, Grosu R, Bartocci E. D. Ratasich, M. Platzer, R. Grosu and E. Bartocci, "Adaptive Fault Detection Exploiting Redundancy with Uncertainties in Space and Time," 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO), Umea, Sweden, 2019, pp. 23-32. https://doi.org/10.1109/SASO.2019.00013.

  112. G. Na, J. Park and Y. Eun, "Attack Resilient State Estimation by Sensor Output Coding," 2019 19th International Conference on Control, Automation and Systems (ICCAS), Jeju, Korea (South), 2019, pp. 1015-1020. https://doi.org/10.23919/ICCAS47443.2019.8971675.

  113. A. A. Jahromi, A. Kemmeugne, D. Kundur and A. Haddadi, Cyber-Physical Attacks Targeting Communication-Assisted Protection Schemes. In IEEE Transactions on Power Systems 2020;35(1):440-450. https://doi.org/10.1109/TPWRS.2019.2924441.

    Article  Google Scholar 

  114. F. Y. Chemashkin and A. A. Zhilenkov, "Fault Tolerance Control in Cyber-Physical Systems," 2019 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus), Saint Petersburg and Moscow, Russia, 2019, pp. 1169-1171. https://doi.org/10.1109/EIConRus.2019.8656639.

  115. B. Chen, N. Pattanaik, A. Goulart, K. L. Butler-purry and D. Kundur, "Implementing attacks for modbus/TCP protocol in a real-time cyber physical system test bed," 2015 IEEE International Workshop Technical Committee on Communications Quality and Reliability (CQR), Charleston, SC, USA, 2015, pp. 1-6. https://doi.org/10.1109/CQR.2015.7129084.

  116. Wu, M, & Moon, YB. "Intrusion Detection of Cyber-Physical Attacks in Manufacturing Systems: A Review." Proceedings of the ASME 2019 International Mechanical Engineering Congress and Exposition. Volume 2B: Advanced Manufacturing. Salt Lake City, Utah, USA. November 11–14, 2019. V02BT02A001. ASME. https://doi.org/10.1115/IMECE2019-10135.

  117. Lee C, Shim H, Eun Y. On redundant observability: from security index to attack detection and resilient state estimation. IEEE Trans Autom Control. 2019;64:775–82. https://doi.org/10.1109/TAC.2018.2837107.

    Article  MathSciNet  MATH  Google Scholar 

  118. Dyka Z, Kabin I, Langendorfer P. Researching resilience a holistic approach. 2019. Z. Dyka, I. Kabin and P. Langendörfer, "Researching Resilience a Holistic Approach," 2019 IEEE East-West Design & Test Symposium (EWDTS), Batumi, Georgia, 2019, pp. 1-4. https://doi.org/10.1109/EWDTS.2019.8884447.

  119. Laszka, A., Abbas, W., Vorobeychik, Y., & Koutsoukos, X.D. (2018). Synergistic Security for the Industrial Internet of Things: Integrating Redundancy, Diversity, and Hardening. 2018 IEEE International Conference on Industrial Internet (ICII), 153-158.

  120. Colombo A, Karnouskos S, Kaynak O, Shi Y, Yin S. Industrial cyberphysical systems: a backbone of the fourth industrial revolution. IEEE Ind Electron Mag. 2017;11:6–16. https://doi.org/10.1109/MIE.2017.2648857.

    Article  Google Scholar 

  121. Kopetz H. Real-time systems: design principles for distributed embedded applications. Berlin: Springer Science & Business Media; 2011.

    Book  MATH  Google Scholar 

  122. Poledna S. Fault-tolerant real-time systems: the problem of replica determinism. Berlin: Springer Science & Business Media; 2007.

    MATH  Google Scholar 

  123. Ntalampiras S. Detection of integrity attacks in cyber-physical critical infrastructures using ensemble modeling. IEEE Trans Ind Inf. 2015;11:104–11. https://doi.org/10.1109/TII.2014.2367322.

    Article  Google Scholar 

  124. Bakirtzis G, Sherburne T, Adams S, Horowitz BM, Beling PA, Fleming CH. An ontological metamodel for cyber-physical system safety, security, and resilience coengineering. arXiv:2006.05304 [Cs]. (2020). http://arxiv.org/abs/2006.05304. Accessed 18 June 2020.

Download references

Author information

Authors and Affiliations

  1. Ecole Centrale de Nantes, LS2N, 44300, Nantes, France

    Yasamin Eslami

  2. Université de Lorraine, CNRS, CRAN, 54000, Nancy, France

    Chiara Franciosi & Mario Lezoche

  3. Independent Researcher, Milan, Italy

    Sahand Ashouri

Authors
  1. Yasamin Eslami
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chiara Franciosi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sahand Ashouri
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mario Lezoche
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yasamin Eslami.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the topical collection “Innovative Intelligent Industrial Production and Logistics 2022” guest edited by Alexander Smirnov, Kurosh Madani, Hervé Panetto and Georg Weichhart.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Eslami, Y., Franciosi, C., Ashouri, S. et al. A Review and Analysis of the Characteristics of Cyber-physical Systems in Industry 4.0. SN COMPUT. SCI. 4, 825 (2023). https://doi.org/10.1007/s42979-023-02268-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s42979-023-02268-0

Keywords

Search

Navigation