Skip to main content

Neural Predictive Monitoring for Collective Adaptive Systems

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13703)

Abstract

Reliable bike-sharing systems can lead to numerous environmental, economic and social benefits and therefore play a central role in the effective development of smart cities. Bike-sharing models deal with spatially distributed stations and interact with an unpredictable environment, the users. Monitoring the trustworthiness of such a collective system is of paramount importance to ensure a good quality of the delivered service, but this task can become computationally demanding due to the complexity of the model under study. Neural Predictive Monitoring (NPM) [5], a neural-network learning-based approach to predictive monitoring (PM) with statistical guarantees, can be employed to preemptively detect violations of a specific requirement – e.g. a station has no more bikes available or a station is full. The computational efficiency of NPM makes PM applicable at runtime even on embedded devices with limited computational power. The goal of this paper is to demonstrate the applicability of NPM on collective adaptive systems such as bike-sharing systems. In particular, we first analyze the performance of NPM over a collective system evolving deterministically. Then, following [7], we tackle a more realistic scenario, where sensors allow only for partial observability and where the system evolves in a stochastic fashion. We evaluate the approach on multiple bike sharing network topologies, obtaining highly accurate predictions and effective error detection rules.

This work has been partially supported by the PRIN project “SEDUCE” n. 2017TWRCNB.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   79.99
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

Learn about institutional subscriptions

Notes

  1. 1.

    The experiments were performed on a computer with a CPU Intel x86, 24 cores and a 128 GB RAM and 15 GB of GPU Tesla V100.

References

  1. Balasubramanian, V., Ho, S.S., Vovk, V.: Conformal Prediction for Reliable Machine Learning: Theory, Adaptations and Applications. Newnes, Oxford (2014)

    MATH  Google Scholar 

  2. Bortolussi, L.: Hybrid limits of continuous time Markov chains. In: 2011 Eighth International Conference on Quantitative Evaluation of Systems, pp. 3–12. IEEE (2011)

    Google Scholar 

  3. Bortolussi, L.: Hybrid behaviour of Markov population models. Inf. Comput. 247, 37–86 (2016)

    Article  MathSciNet  Google Scholar 

  4. Bortolussi, L., Cairoli, F., Paoletti, N., Smolka, S.A., Stoller, S.D.: Neural predictive monitoring. In: Finkbeiner, B., Mariani, L. (eds.) RV 2019. LNCS, vol. 11757, pp. 129–147. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32079-9_8

    Chapter  Google Scholar 

  5. Bortolussi, L., Cairoli, F., Paoletti, N., Smolka, S.A., Stoller, S.D.: Neural predictive monitoring and a comparison of frequentist and Bayesian approaches. Int. J. Softw. Tools Technol. Transf. 23(4), 615–640 (2021)

    Article  Google Scholar 

  6. Bortolussi, L., Hillston, J., Latella, D., Massink, M.: Continuous approximation of collective system behaviour: a tutorial. Perform. Eval. 70(5), 317–349 (2013)

    Article  Google Scholar 

  7. Cairoli, F., Bortolussi, L., Paoletti, N.: Neural predictive monitoring under partial observability. In: Feng, L., Fisman, D. (eds.) RV 2021. LNCS, vol. 12974, pp. 121–141. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-88494-9_7

    Chapter  Google Scholar 

  8. Gillespie, D.T., Petzold, L.: Numerical simulation for biochemical kinetics. In: Systems Modelling in Cellular Biology, pp. 331–354 (2006)

    Google Scholar 

  9. Le Boudec, J.Y., McDonald, D., Mundinger, J.: A generic mean field convergence result for systems of interacting objects. In: Fourth International Conference on the Quantitative Evaluation of Systems (QEST 2007), pp. 3–18. IEEE (2007)

    Google Scholar 

  10. Lehmann, E.L., Romano, J.P.: Testing Statistical Hypotheses. Springer, New York (2006). https://doi.org/10.1007/0-387-27605-X

    Book  MATH  Google Scholar 

  11. Papadopoulos, H.: Inductive conformal prediction: theory and application to neural networks. In: Tools in Artificial Intelligence. InTech (2008)

    Google Scholar 

  12. Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS-W (2017)

    Google Scholar 

  13. Younes, H.L., Simmons, R.G.: Statistical probabilistic model checking with a focus on time-bounded properties. Inf. Comput. 204(9), 1368–1409 (2006)

    Article  MathSciNet  Google Scholar 

  14. Zuliani, P., Platzer, A., Clarke, E.M.: Bayesian statistical model checking with application to simulink/stateflow verification. In: Proceedings of the 13th ACM International Conference on Hybrid Systems: Computation and Control, pp. 243–252 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Francesca Cairoli .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Cairoli, F., Paoletti, N., Bortolussi, L. (2022). Neural Predictive Monitoring for Collective Adaptive Systems. In: Margaria, T., Steffen, B. (eds) Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning. ISoLA 2022. Lecture Notes in Computer Science, vol 13703. Springer, Cham. https://doi.org/10.1007/978-3-031-19759-8_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19759-8_3

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19758-1

  • Online ISBN: 978-3-031-19759-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics