Skip to main content

Spatio-Temporal Model-Checking of Cyber-Physical Systems Using Graph Queries

  • Conference paper
  • First Online:
Tests and Proofs (TAP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12165))

Included in the following conference series:

  • 429 Accesses

Abstract

We explore the application of graph database technology to spatio-temporal model checking of cooperating cyber-physical systems-of- systems such as vehicle platoons. We present a translation of spatio-temporal automata (STA) and the spatio-temporal logic STAL to semantically equivalent property graphs and graph queries respectively. We prove a sound reduction of the spatio-temporal verification problem to graph database query solving. The practicability and efficiency of this approach is evaluated by introducing NeoMC, a prototype implementation of our explicit model checking approach based on Neo4j. To evaluate NeoMC we consider case studies of verifying vehicle platooning models. Our evaluation demonstrates the effectiveness of our approach in terms of execution time and counterexample detection.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Notes

  1. 1.

    To ensure decidability, STAL is syntactically restricted so that quantification over data types is not allowed.

  2. 2.

    This definition clearly generalises to the n-dimensional case.

  3. 3.

    denoted as “(key,value)”.

  4. 4.

    Note: we can derive relative velocity from absolute velocity, and both measurements are always bounded in practise.

  5. 5.

    In this figure, \(n_i\in N \), \(e_i\in \textit{E}\), \(\textit{L}=\{\)\(\textit{State}\)\(\}\), \(\textit{T}=\{``\textit{Next}"\}\), \(\textit{Lab}(n_i)=\)\(\textit{State}\)” and \(\textit{Typ}(e_i)=\)\(\textit{Next}\)”.

  6. 6.

    By the construction rules of Fig. 8, \(\mathcal {G}_{A}\) is essentially structurally isomorphic to A.

References

  1. Khosrowjerdi, H., Meinke, K.: Learning-based testing for autonomous systems using spatial and temporal requirements. In: Proceedings of the 1st International Workshop on Machine Learning and Software Engineering in Symbiosis, MASES@ASE 2018, Montpellier, France, 3 September 2018, pp. 6–15 (2018). https://doi.org/10.1145/3243127.3243129

  2. Kamali, M., Linker, S., Fisher, M.: Modular verification of vehicle platooning with respect to decisions, space and time. In: Artho, C., Ölveczky, P.C. (eds.) FTSCS 2018. CCIS, vol. 1008, pp. 18–36. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12988-0_2

    Chapter  Google Scholar 

  3. Schwammberger, M.: An abstract model for proving safety of autonomous urban traffic. Theor. Comput. Sci. 744, 143–169 (2018). https://doi.org/10.1016/j.tcs.2018.05.028

    Article  MathSciNet  MATH  Google Scholar 

  4. Alur, R., Dill, D.L.: A theory of timed automata. Theor. Comput. Sci. 126(2), 183–235 (1994). https://doi.org/10.1016/0304-3975(94)90010-8

    Article  MathSciNet  MATH  Google Scholar 

  5. Chaochen, Z., Hoare, C., Ravn, A.P.: A calculus of durations. Inf. Process. Lett. 40(5), 269–276 (1991). http://www.sciencedirect.com/science/article/pii/002001909190122X

    Article  MathSciNet  Google Scholar 

  6. Haghighi, I., Jones, A., Kong, Z., Bartocci, E., Grosu, R., Belta, C.: Spatel: a novel spatial-temporal logic and its applications to networked systems. In: Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control, HSCC 2015, Seattle, WA, USA, 14–16 April 2015, pp. 189–198 (2015). https://doi.org/10.1145/2728606.2728633

  7. Quesel, J.-D., Schäfer, A.: Spatio-temporal model checking for mobile real-time systems. In: Barkaoui, K., Cavalcanti, A., Cerone, A. (eds.) ICTAC 2006. LNCS, vol. 4281, pp. 347–361. Springer, Heidelberg (2006). https://doi.org/10.1007/11921240_24

    Chapter  Google Scholar 

  8. Angles, R., Arenas, M., Barceló, P., Hogan, A., Reutter, J.L., Vrgoc, D.: Foundations of modern query languages for graph databases. ACM Comput. Surv. 50(5), 68:1–68:40 (2017). http://doi.acm.org/10.1145/3104031

    Article  Google Scholar 

  9. Bennaceur, A., Hähnle, R., Meinke, K. (eds.): Machine Learning for Dynamic Software Analysis: Potentials and Limits. LNCS, vol. 11026. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-96562-8

    Book  Google Scholar 

  10. Meinke, K., Niu, F.: A learning-based approach to unit testing of numerical software. In: Petrenko, A., Simão, A., Maldonado, J.C. (eds.) ICTSS 2010. LNCS, vol. 6435, pp. 221–235. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16573-3_16

    Chapter  Google Scholar 

  11. Meinke, K., Sindhu, M.A.: Incremental learning-based testing for reactive systems. In: Gogolla, M., Wolff, B. (eds.) TAP 2011. LNCS, vol. 6706, pp. 134–151. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21768-5_11

    Chapter  Google Scholar 

  12. Webber, J.: A programmatic introduction to neo4j. In: Conference on Systems, Programming, and Applications: Software for Humanity, SPLASH 2012, Tucson, AZ, USA, 21–25 October 2012, pp. 217–218 (2012). https://doi.org/10.1145/2384716.2384777

  13. Francis, N., et al.: Cypher: an evolving query language for property graphs. In: Proceedings of the 2018 International Conference on Management of Data, SIGMOD Conference 2018, Houston, TX, USA, 10–15 June 2018, pp. 1433–1445 (2018). http://doi.acm.org/10.1145/3183713.3190657

  14. de la Higuera, C.: Grammatical Inference: Learning Automata and Grammars. Cambridge University Press, (2010). iv + 417 pages, Machine Translation, vol. 24, no. 3–4, pp. 291–293, 2010. https://doi.org/10.1007/s10590-011-9086-9

  15. Angles, R., Gutiérrez, C.: Survey of graph database models. ACM Comput. Surv. 40(1), 11–139 (2008). https://doi.org/10.1145/1322432.1322433

    Article  Google Scholar 

  16. Robinson, I., Webber, J., Eifrem, E.: Graph Databases: New Opportunities for Connected Data, 2nd edn. O’Reilly Media Inc., Sebastopol (2015)

    Google Scholar 

  17. Hölsch, J., Schmidt, T., Grossniklaus, M.: On the performance of analytical and pattern matching graph queries in neo4j and a relational database. In: Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017), Venice, Italy, 21–24 March 2017 (2017). http://ceur-ws.org/Vol-1810/GraphQ_paper_01.pdf

  18. Francis, N., et al.: Formal semantics of the language cypher. CoRR, vol. abs/1802.09984 (2018). http://arxiv.org/abs/1802.09984

  19. Junghanns, M., Kießling, M., Averbuch, A., Petermann, A., Rahm, E.: Cypher-based graph pattern matching in gradoop. In: Proceedings of the Fifth International Workshop on Graph Data-management Experiences & Systems, GRADES@SIGMOD/PODS 2017, Chicago, IL, USA, 14–19 May 2017, pp. 3:1–3:8 (2017). http://doi.acm.org/10.1145/3078447.3078450

  20. Clarke, E.M., Henzinger, T.A., Veith, H., Bloem, R. (eds.): Handbook of Model Checking. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-10575-8

    Book  MATH  Google Scholar 

  21. Wolper, P., Vardi, M.Y., Sistla, A.P.: Reasoning about infinite computation paths (extended abstract). In: 24th Annual Symposium on Foundations of Computer Science, Tucson, Arizona, USA, 7–9 November 1983, pp. 185–194 (1983). https://doi.org/10.1109/SFCS.1983.51

  22. Alpern, B., Schneider, F.B.: Defining liveness. Inf. Process. Lett. 21(4), 181–185 (1985). https://doi.org/10.1016/0020-0190(85)90056-0

    Article  MathSciNet  MATH  Google Scholar 

  23. Vardi, M.Y., Wolper, P.: An automata-theoretic approach to automatic program verification (preliminary report). In: Proceedings of the Symposium on Logic in Computer Science (LICS 1986), Cambridge, Massachusetts, USA, June 16–18, 1986, pp. 332–344 (1986)

    Google Scholar 

  24. Búr, M., Szilágyi, G., Vörös, A., Varró, D.: Distributed graph queries for runtime monitoring of cyber-physical systems. In: Russo, A., Schürr, A. (eds.) FASE 2018. LNCS, vol. 10802, pp. 111–128. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-89363-1_7

    Chapter  Google Scholar 

  25. Meinke, K.: Learning-based testing of cyber-physical systems-of-systems: a platooning study. In: Reinecke, P., Di Marco, A. (eds.) EPEW 2017. LNCS, vol. 10497, pp. 135–151. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66583-2_9

    Chapter  Google Scholar 

  26. Cavada, R., Cimatti, A., Dorigatti, M., Griggio, A., Mariotti, A., Micheli, A., Mover, S., Roveri, M., Tonetta, S.: The nuXmv symbolic model checker. In: Biere, A., Bloem, R. (eds.) CAV 2014. LNCS, vol. 8559, pp. 334–342. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-08867-9_22

    Chapter  Google Scholar 

  27. Holzmann, G.J.: The SPIN Model Checker - Primer and Referencemanual. Addison-Wesley, Boston (2004)

    Google Scholar 

  28. Kant, G., Laarman, A., Meijer, J., van de Pol, J., Blom, S., van Dijk, T.: LTSmin: high-performance language-independent model checking. In: Baier, C., Tinelli, C. (eds.) TACAS 2015. LNCS, vol. 9035, pp. 692–707. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46681-0_61

    Chapter  Google Scholar 

  29. Chiarugi, D., Falaschi, M., Hermith, D., Olarte, C.: Verification of spatial and temporal modalities in biochemical systems. Electr. Notes Theor. Comput. Sci. 316, 29–44 (2015). https://doi.org/10.1016/j.entcs.2015.06.009

    Article  MATH  Google Scholar 

  30. Parvu, O., Gilbert, D.R.: Automatic validation of computational models using pseudo-3D Spatio-temporal model checking. BMC Syst. Biol. 8, 124 (2014). https://doi.org/10.1186/s12918-014-0124-0

    Article  Google Scholar 

  31. Grosu, R., Smolka, S.A., Corradini, F., Wasilewska, A., Entcheva, E., Bartocci, E.: Learning and detecting emergent behavior in networks of cardiac myocytes. Commun. ACM 52(3), 97–105 (2009). https://doi.org/10.1145/1467247.1467271

    Article  MATH  Google Scholar 

  32. de Oliveira, Í.R., Cugnasca, P.S.: Checking safe trajectories of aircraft using hybrid automata. In: Anderson, S., Felici, M., Bologna, S. (eds.) SAFECOMP 2002. LNCS, vol. 2434, pp. 224–235. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45732-1_22

    Chapter  Google Scholar 

  33. Ciancia, V., Grilletti, G., Latella, D., Loreti, M., Massink, M.: An experimental spatio-temporal model checker. In: Bianculli, D., Calinescu, R., Rumpe, B. (eds.) SEFM 2015. LNCS, vol. 9509, pp. 297–311. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-49224-6_24

    Chapter  Google Scholar 

  34. Ciancia, V., Gilmore, S., Grilletti, G., Latella, D., Loreti, M., Massink, M.: Spatio-temporal model checking of vehicular movement in public transport systems. STTT 20(3), 289–311 (2018). https://doi.org/10.1007/s10009-018-0483-8

    Article  Google Scholar 

Download references

Acknowledgments

This research has been supported by KTH ICT-TNG project STaRT (Spatio-Temporal Planning at Runtime), as well as the German Federal Ministry of Education and Research (BMBF) through funding for the CISPA-Stanford Center for Cybersecurity (FKZ: 13N1S0762).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hojat Khosrowjerdi .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Khosrowjerdi, H., Nemati, H., Meinke, K. (2020). Spatio-Temporal Model-Checking of Cyber-Physical Systems Using Graph Queries. In: Ahrendt, W., Wehrheim, H. (eds) Tests and Proofs. TAP 2020. Lecture Notes in Computer Science(), vol 12165. Springer, Cham. https://doi.org/10.1007/978-3-030-50995-8_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-50995-8_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-50994-1

  • Online ISBN: 978-3-030-50995-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics