Skip to main content
Log in

Performance modeling and quantitative evaluation for cyber-physical systems based on LTS

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Cyber-physical systems (CPS) are complex systems that can be understood as a hybrid system, a real-time system with probabilistic behavior, and a concurrent system. With the increasing use of CPS, there is a growing demand for higher reliability and performance. Additionally, the large amount of information in CPS requires processing, exacerbating the state space explosion problem caused by model checking methods used for the quantitative evaluation of CPS. Therefore, evaluating the functionality, performance, and reliability of CPS is not only an important research topic but also an inevitable challenge. This paper aims to establish a comprehensive and efficient CPS performance model and a quantitative evaluation method and proposes solutions to existing problems based on the specific practical application environment of the system. First, we extend the labeled transition system (LTS) and propose a hybrid probability time cost transition system (HPTCTS) that provides a detailed description of the continuous behavior, probabilistic behavior, time characteristics, and performance of CPS. Furthermore, we propose HPTCTS temporal logic (HPTCTS-TL) to describe the performance characteristic of HPTCTS. To alleviate the state space explosion problem when quantitatively evaluating the HPTCTS model, we propose the corresponding quantitative evaluation algorithm based on symbolic model checking. Finally, we discuss a typical example of CPS to demonstrate the feasibility of our approach. Overall, our work contributes to a better understanding of CPS and provides a more effective and comprehensive way to evaluate CPS performance, which is crucial for the successful development and deployment of CPS in real-world applications.

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

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data availability

This declaration is not applicable.

References

  1. Pivoto DGS, de Almeida LFF, da Rosa Righi R, Rodrigues JJPC, Lugli AB, Alberti AM (2021) Cyber-physical systems architectures for industrial internet of things applications in industry 4.0: a literature review. J Manuf Syst 58:176–192. https://doi.org/10.1016/j.jmsy.2020.11.017

    Article  Google Scholar 

  2. Tyagi AK, Sreenath N (2021) Cyber physical systems: analyses, challenges and possible solutions. Internet Things Cyber-Phys Syst 1:22–33. https://doi.org/10.1016/j.iotcps.2021.12.002

    Article  Google Scholar 

  3. Yaacoub J-PA, Salman O, Noura HN, Kaaniche N, Chehab A, Malli M (2020) Cyber-physical systems security: limitations, issues and future trends. Microprocessors Microsyst 77:103201. https://doi.org/10.1016/j.micpro.2020.103201

    Article  Google Scholar 

  4. Sanislav T, Zeadally S, Mois GD, Fouchal H (2019) Reliability, failure detection and prevention in cyber-physical systems (cpss) with agents. Concurr Comput: Pract Exp 31(24):4481. https://doi.org/10.1002/cpe.4481

    Article  Google Scholar 

  5. Yang C, Sun H, Liu J, Kang J, Yin W, Wang H, Li T (2021) Uncertainty modeling and quantitative evaluation of cyber-physical systems. In: 2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC), pp 874–883. https://doi.org/10.1109/COMPSAC51774.2021.00120

  6. Baier C, Katoen J-P (2008) Principles of model checking, vol 26202649

  7. Clarke EM, Henzinger TA, Veith H, Bloem R (eds) (2018) Handbook of model checking. Springer, Cham. https://doi.org/10.1007/978-3-319-10575-8

  8. Rashid A, Hasan O (2021) Formal analysis of the continuous dynamics of cyber–physical systems using theorem proving. J Syst Architect 112:101850. https://doi.org/10.1016/j.sysarc.2020.101850

    Article  Google Scholar 

  9. Fränzle M, Shirmohammadi M, Swaminathan M, Worrell J (2022) Costs and rewards in priced timed automata. Inf Comput 282:104656. https://doi.org/10.1016/j.ic.2020.104656

    Article  MathSciNet  Google Scholar 

  10. Zhang X, Li J (2021) Power control for cognitive users of perception layer in complex industrial cps based on dqn. IEEE Access 9:25371–25382. https://doi.org/10.1109/ACCESS.2021.3057911

    Article  Google Scholar 

  11. Tei K, Tahara Y, Ohsuga A (2022) Towards scalable model checking of reflective systems via labeled transition systems. IEEE Trans Software Eng. https://doi.org/10.1109/TSE.2022.3174408

    Article  Google Scholar 

  12. Yang Y, Zu Q, Ke W, Zhang M, Li X (2019) Real-time system modeling and verification through labeled transition system analyzer. IEEE Access 7:26314–26323. https://doi.org/10.1109/ACCESS.2019.2899761

    Article  Google Scholar 

  13. Rao L, Liu S, Peng H (2022) An integrated formal method combining labeled transition system and event-b for system model refinement. IEEE Access 10:13089–13102. https://doi.org/10.1109/ACCESS.2022.3146390

    Article  Google Scholar 

  14. Cleaveland R, Roscoe AW, Smolka SA (2018) Process algebra and model checking. In: Clarke EM, Henzinger TA, Veith H, Bloem R (eds) Handbook of model checking. Springer, Cham, pp 1149–1195. https://doi.org/10.1007/978-3-319-10575-8_32

  15. Baier C, Haverkort BR, Hermanns H, Katoen J-P (2010) Performance evaluation and model checking join forces. Commun ACM 53(9):76–85. https://doi.org/10.1145/1810891.1810912

    Article  Google Scholar 

  16. Baier C, Cloth L, Haverkort BR, Hermanns H, Katoen J-P (2010) Performability assessment by model checking of Markov reward models. Formal Methods Syst Design 36(1):1–36. https://doi.org/10.1007/s10703-009-0088-7

    Article  Google Scholar 

  17. Qian L, Liu J (2020) Safe reinforcement learning via probabilistic timed computation tree logic. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp 1–8. https://doi.org/10.1109/IJCNN48605.2020.9207384

  18. Henzinger TA, Nicollin X, Sifakis J, Yovine S (1994) Symbolic model checking for real-time systems. Inf Comput 111(2):193–244. https://doi.org/10.1006/inco.1994.1045

    Article  MathSciNet  Google Scholar 

  19. Chaki S, Gurfinkel A (2018) Bdd-based symbolic model checking. In: Clarke EM, Henzinger TA, Veith H, Bloem R (eds) Handbook of model checking. Springer, Cham, pp 219–245. https://doi.org/10.1007/978-3-319-10575-8_8

  20. Clarke EM, Grumberg O, Mcmillan KL, Zhao X (2003) Efficient generation of counterexamples and witnesses in symbolic model checking. International Journal on Software Tools for Technology Transfer (STTT). https://doi.org/10.1007/11513988_9

  21. Ciesinski F, Baier C, Größer M, Parker D (2008) Generating compact mtbdd-representations from probmela specifications. In: Havelund K, Majumdar R, Palsberg J (eds) Model checking software. Lecture Notes in Computer Science. Springer, Berlin, Heidelberg, pp 60–76. https://doi.org/10.1007/978-3-540-85114-1_7

  22. Hermanns H, Kwiatkowska M, Norman G, Parker D, Siegle M (2003) On the use of mtbdds for performability analysis and verification of stochastic systems. J Logic Algebraic Program 56(1):23–67. https://doi.org/10.1016/S1567-8326(02)00066-8

    Article  MathSciNet  Google Scholar 

  23. Mikusek P (2009) Multi-terminal bdd synthesis and applications. In: 2009 International Conference on Field Programmable Logic and Applications, pp 721–722. https://doi.org/10.1109/FPL.2009.5272326

  24. Fujita M, McGeer PC, Yang JC-Y (1997) Multi-terminal binary decision diagrams: an efficient data structure for matrix representation. Formal Methods Syst Design 10(2):149–169. https://doi.org/10.1023/A:1008647823331

    Article  Google Scholar 

  25. Stefanakos I, Calinescu R, Douthwaite J, Aitken J, Law J (2022) Safety controller synthesis for a mobile manufacturing cobot. In: Schlingloff B-H, Chai M (eds) Software engineering and formal methods, vol 13550, pp 271–287. Springer, Cham. https://doi.org/10.1007/978-3-031-17108-6_17

  26. Debbi H (2021) Modeling and performance analysis of resource provisioning in cloud computing using probabilistic model checking. Informatica. https://doi.org/10.31449/inf.v45i4.3308

  27. Guo X (2019) Performance analysis of Israeli–Jalfon’s algorithm using probabilistic model checking. Concurr Comput: Pract Exp 31(9):4973. https://doi.org/10.1002/cpe.4973

    Article  Google Scholar 

  28. Kwiatkowska M, Norman G, Sproston J, Wang F (2007) Symbolic model checking for probabilistic timed automata. Inf Comput 205(7):1027–1077. https://doi.org/10.1016/j.ic.2007.01.004

    Article  MathSciNet  Google Scholar 

  29. Fränzle M, Hahn EM, Hermanns H, Wolovick N, Zhang L (2011) Measurability and safety verification for stochastic hybrid systems. In: Proceedings of the 14th International Conference on Hybrid Systems: Computation and Control - HSCC ’11. ACM Press, Chicago, IL, USA, , p 43. https://doi.org/10.1145/1967701.1967710

  30. Clarke EM (1997) Model checking. In: Foundations of Software Technology and Theoretical Computer Science: 17th Conference Kharagpur, India, December 18–20, 1997 Proceedings 17. Springer, pp 54–56

  31. Shetty J, Lawson CP, Shahneh AZ (2015) Simulation for temperature control of a military aircraft cockpit to avoid pilot’s thermal stress. CEAS Aeronaut J 6:319–333

    Article  Google Scholar 

  32. Lanotte R, Merro M, Tini S (2021) A probabilistic calculus of cyber-physical systems. Inf Comput 279:104618. https://doi.org/10.1016/j.ic.2020.104618

    Article  MathSciNet  Google Scholar 

  33. Du D, Huang P, Jiang K, Mallet F (2018) pcssl: A stochastic extension to marte/ccsl for modeling uncertainty in cyber physical systems. Sci Comput Program 166:71–88. https://doi.org/10.1016/j.scico.2018.05.005

    Article  Google Scholar 

  34. Basile D, Di Giandomenico F, Gnesi S (2019) On quantitative assessment of reliability and energy consumption indicators in railway systems. In: Kharchenko V, Kondratenko Y, Kacprzyk J (eds) Green IT Engineering: Social, Business and Industrial Applications. Springer, Cham, pp 423–447. https://doi.org/10.1007/978-3-030-00253-4_18

  35. Parker DA (2003) Implementation of symbolic model checking for probabilistic systems. PhD thesis, University of Birmingham

Download references

Acknowledgements

The authors express their gratitude to the editors and referees for their encouragement and constructive comments on this article.

Funding

This work was supported in part by the Aviation Science Foundation of China under Grant 20185152035, in part by the National Natural Science Foundation of China under Grant 61572253, and in part by the Fundamental Research Funds for the Central Universities under Grants NJ2020022 and NJ2019010.

Author information

Authors and Affiliations

Authors

Contributions

Zhen Li contributed to conceptualization, methodology, validation, formal analysis, investigation, resources, data curation, writing -original draft, writing-review & editing, visualization, project administration. Zining Cao contributed to conceptualization, methodology, validation, formal analysis, writing-review & editing, supervision, project administration, funding acquisition. Chao Xing contributed to validation, investigation, writing-review & editing.

Corresponding author

Correspondence to Zining Cao.

Ethics declarations

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Conflict of interest

The authors declare that they have no known competing fnancial interests or personal relationships that could have appeared to infuence the work reported in this paper.

Additional information

Publisher's Note

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

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

Li, Z., Cao, Z. & Xing, C. Performance modeling and quantitative evaluation for cyber-physical systems based on LTS. J Supercomput 80, 5616–5653 (2024). https://doi.org/10.1007/s11227-023-05669-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05669-3

Keywords