Skip to main content
SpringerLink
Log in

Identify spatio-temporal properties of network traffic by model checking

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

Abstract

Cellular networks have been widely deployed and are under ever-growing pressure from increasing energy consumption. Identifying the spatio-temporal properties of network traffic is crucial to effectively manage large-scale cellular networks and to optimize energy control strategies. However, traditional methods need to construct complex mathematical models to describe the properties of traffic, making the process inefficient and not automatic. Recent methods based on deep learning techniques are unexplainable and untraceable. In this paper, we propose a novel modeling and analysis approach by applying the spatio-temporal model checking technique to the identification of network traffic properties. First, we model the spatial structure of the cellular network by a closure space and the temporal structure of network traffic by the Kripke structure. Second, we provide logical characterizations of the spatio-temporal properties of network traffic by suitable spatio-temporal logic of closure space (STLCS) formulas. Third, we use model checking algorithms to detect the spatio-temporal properties of network traffic and to visualize the results. The experiments are illustrated with the Milan network traffic dataset and indicate that our approach can automatically and effectively detect desirable spatio-temporal properties of cellular network traffic.

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
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

Availability of data and materials

The dataset is released under the Open Database License (ODbL) and is publicly available in the Harvard Dataverse. The link to the dataset is https://doi.org/10.7910/dvn/QLCABU (2015) and https://doi.org/10.7910/dvn/EGZHFV (2015).

References

  1. Liu X, Ansari N (2018) Dual-battery enabled profit driven user association in green heterogeneous cellular networks. IEEE Trans Green Commun Netw 2(4):1002–1011. https://doi.org/10.1109/TGCN.2018.2869039

    Article  Google Scholar 

  2. Abbasi M, Shahraki A, Taherkordi A (2021) Deep learning for network traffic monitoring and analysis (NTMA: a survey. Comput Commun 170:19–41. https://doi.org/10.1016/j.comcom.2021.01.021

    Article  Google Scholar 

  3. Cecil A (2006) A summary of network traffic monitoring and analysis techniques. Computer systems analysis, pp 4–7

  4. D’Alconzo A, Drago I, Morichetta A et al (2019) A survey on big data for network traffic monitoring and analysis. IEEE Trans Netw Serv Manag 16(3):800–813. https://doi.org/10.1109/TNSM.2019.2933358

    Article  Google Scholar 

  5. Wang J, Tang J, Xu Z et al (2017) Spatiotemporal modeling and prediction in cellular networks: a big data enabled deep learning approach. In: IEEE INFOCOM 2017-IEEE Conference on Computer Communications. IEEE, pp 1–9. https://doi.org/10.1109/INFOCOM.2017.8057090

  6. Barrat A, Barthélemy M, Vespignani A (2008) Dynamical processes on complex networks. Cambridge University Press, Cambridge. https://doi.org/10.1017/CBO9780511791383.020

    Book  MATH  Google Scholar 

  7. Newman M (2010) Networks: an introduction. Oxford University Press, Oxford. https://doi.org/10.1093/acprof:oso/9780199206650.001.0001

    Book  MATH  Google Scholar 

  8. Wang Y, Wei Z, Cao J (2020) Epidemic dynamics of influenza-like diseases spreading in complex networks. Nonlinear Dyn 101:1801–1820. https://doi.org/10.1007/s11071-020-05867-1

    Article  MATH  Google Scholar 

  9. Shafiq MZ, Ji L, Liu AX et al (2012) Characterizing geospatial dynamics of application usage in a 3G cellular data network. In: 2012 Proceedings IEEE INFOCOM. IEEE, pp 1341–1349. https://doi.org/10.1109/INFCOM.2012.6195497

  10. Nika A, Ismail A, Zhao BY et al (2016) Understanding and predicting data hotspots in cellular networks. Mobile Netw Appl 21:402–413. https://doi.org/10.1007/s11036-015-0648-6

    Article  Google Scholar 

  11. Zhou Y, Zhao Z, Li R et al (2017) Cooperation-based probabilistic caching strategy in clustered cellular networks. IEEE Commun Lett 21(9):2029–2032. https://doi.org/10.1109/LCOMM.2017.2717398

    Article  Google Scholar 

  12. Zhou L, Chen X (2019) SVM hotspot identification for cellular networks. In: 2019 IEEE 5th International Conference on Computer and Communications (ICCC). IEEE, pp 1103–1107. https://doi.org/10.1109/ICCC47050.2019.9064447

  13. Masood U, Asghar A, Imran A et al (2018) Deep learning based detection of sleeping cells in next generation cellular networks. In: 2018 IEEE Global Communications Conference (GLOBECOM). IEEE, pp 206–212. https://doi.org/10.1109/GLOCOM.2018.8647689

  14. Zhou L, Chen X, Dong R et al (2020) Hotspots prediction based on LSTM neural network for cellular networks. J Phys Conf Ser. https://doi.org/10.1088/1742-6596/1624/5/052016

    Article  Google Scholar 

  15. Zhang C, Patras P (2018) Long-term mobile traffic forecasting using deep spatio-temporal neural networks. In: Proceedings of the Eighteenth ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp 231–240. https://doi.org/10.1145/3209582.3209606

  16. D’Angelo G, Palmieri F (2021) Network traffic classification using deep convolutional recurrent autoencoder neural networks for spatial-temporal features extraction. J Netw Comput Appl 173:102890. https://doi.org/10.1016/j.jnca.2020.102890

    Article  Google Scholar 

  17. Gao H, Zhang Y, Miao H et al (2021) SDTIOA: modeling the timed privacy requirements of IoT service composition: a user interaction perspective for automatic transformation from BPEL to timed automata. Mobile Netw Appl. https://doi.org/10.1007/s11036-021-01846-x

    Article  Google Scholar 

  18. Gao H, Dai B, Miao H et al (2023) A novel GAPG approach to automatic property generation for formal verification: the GAN perspective. ACM Trans Multimed Comput Commun Appl 19(1):1–22. https://doi.org/10.1145/3517154

    Article  Google Scholar 

  19. Hussain SR, Echeverria M, Karim I et al (2019) 5Greasoner: a property-directed security and privacy analysis framework for 5g cellular network protocol. In: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security. ACM, pp 669–684. https://doi.org/10.1145/3319535.3354263

  20. Zroug S, Kahloul L, Benharzallah S et al (2021) A hierarchical formal method for performance evaluation of WSNS protocol. Computing 103(6):1183–1208. https://doi.org/10.1007/s00607-020-00898-3

    Article  MathSciNet  MATH  Google Scholar 

  21. Hou K, Li Y, Yu Y et al (2021) Discovering emergency call pitfalls for cellular networks with formal methods. In: Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services, pp 296–309. https://doi.org/10.1145/3458864.3466625

  22. Cai X, John W, Meirosu C (2018) Automatic data aggregation for recursively modeled NFV services. Int J Netw Manag 28(2):e2009. https://doi.org/10.1002/nem.2009

    Article  Google Scholar 

  23. Baier C, Katoen JP (2008) Principles of model checking. MIT Press, Cambridge

    MATH  Google Scholar 

  24. van Benthem J, Bezhanishvili G (2007) Modal logics of space. In: Aiello M, Pratt-Hartmann I, Van Benthem J (eds) Handbook of spatial logics. Springer, Dordrecht, pp 217–298. https://doi.org/10.1007/978-1-4020-5587-4_5

    Chapter  Google Scholar 

  25. Massink M, Loreti M, Latella D et al (2017) Model checking spatial logics for closure spaces. Log Methods Comput Sci. https://doi.org/10.2168/LMCS-12(4:2)2016

    Article  MATH  Google Scholar 

  26. Ciancia V, Grilletti G, Latella D et al (2015) An experimental spatio-temporal model checker. In: Bianculli D, Calinescu R, Rumpe B (eds) SEFM 2015 collocated workshops. Springer, Berlin, pp 297–311. https://doi.org/10.1007/978-3-662-49224-6_24

    Chapter  Google Scholar 

  27. Loreti M, Bortolussi L, Bartocci E et al (2022) A logic for monitoring dynamic networks of spatially-distributed cyber-physical systems. Log Methods Comput Sci. https://doi.org/10.46298/LMCS-18(1:4)2022

    Article  MathSciNet  MATH  Google Scholar 

  28. Banci Buonamici F, Belmonte G, Ciancia V et al (2020) Spatial logics and model checking for medical imaging. Int J Softw Tools Technol Transf 22:195–217. https://doi.org/10.1007/s10009-019-00511-9

    Article  Google Scholar 

  29. Ciancia V, Latella D, Massink M et al (2015) Exploring spatio-temporal properties of bike-sharing systems. In: 2015 IEEE International Conference on Self-Adaptive and Self-Organizing Systems Workshops. IEEE, pp 74–79. https://doi.org/10.1109/SASOW.2015.17

  30. Ciancia V, Latella D, Massink M et al (2016) A tool-chain for statistical spatio-temporal model checking of bike sharing systems. In: International Symposium on Leveraging Applications of Formal Methods. Springer, pp 657–673. https://doi.org/10.1007/978-3-319-47166-2_46

  31. Ciancia V, Gilmore S, Grilletti G et al (2018) Spatio-temporal model checking of vehicular movement in public transport systems. Int J Softw Tools Technol Transf 20(3):289–311. https://doi.org/10.1007/s10009-018-0483-8

    Article  Google Scholar 

  32. Bartocci E, Bortolussi L, Loreti M et al (2017) Monitoring mobile and spatially distributed cyber-physical systems. In: Proceedings of the 15th ACM-IEEE International Conference on Formal Methods and Models for System Design, pp 146–155. https://doi.org/10.1145/3127041.3127050

  33. Vana L, Visconti E, Nenzi L et al (2021) Posterior predictive model checking using formal methods in a spatio-temporal model. arXiv preprint arXiv:2110.01360

  34. Wang H, Ding J, Li Y et al (2015) Characterizing the spatio-temporal inhomogeneity of mobile traffic in large-scale cellular data networks. In: Proceedings of the 7th International Workshop on Hot Topics in Planet-Scale MObile Computing and Online Social NeTworking. ACM, pp 19–24. https://doi.org/10.1145/2757513.2757518

  35. Xu F, Lin Y, Huang J et al (2016) Big data driven mobile traffic understanding and forecasting: a time series approach. IEEE Trans Serv Comput 9(5):796–805. https://doi.org/10.1109/TSC.2016.2599878

    Article  Google Scholar 

  36. Laner M, Svoboda P, Schwarz S et al (2012) Users in cells: a data traffic analysis. In: 2012 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, pp 3063–3068. https://doi.org/10.1109/WCNC.2012.6214330

  37. Salahdine F, Opadere J, Liu Q et al (2021) A survey on sleep mode techniques for ultra-dense networks in 5G and beyond. Comput Netw 201:108567. https://doi.org/10.1016/j.comnet.2021.108567

    Article  Google Scholar 

  38. Debaillie B, Desset C, Louagie F (2015) A flexible and future-proof power model for cellular base stations. In: 2015 IEEE 81st Vehicular Technology Conference (VTC Spring). IEEE, pp 1–7. https://doi.org/10.1109/VTCSpring.2015.7145603

  39. Barlacchi G, De Nadai M, Larcher R et al (2015) A multi-source dataset of urban life in the city of Milan and the province of Trentino. Sci. Data 2(1):1–15. https://doi.org/10.1038/sdata.2015.55

    Article  Google Scholar 

  40. Gao H, Liu C, Li Y et al (2020) V2VR: reliable hybrid-network-oriented V2V data transmission and routing considering RSUS and connectivity probability. IEEE Trans Intell Transp Syst 22(6):3533–3546. https://doi.org/10.1109/TITS.2020.2983835

    Article  Google Scholar 

Download references

Acknowledgements

We are very grateful to the editors and reviewers for their comments on this manuscript. This work was supported by the Ningbo Natural Science Foundation of China (Grant No. 2019A610088), the Open Subject of Key Laboratory of Embedded and Service Computing of Ministry of Education of China (Grant No. ESSCKF 2019-07), the Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application.

Funding

This work was supported by the Ningbo Natural Science Foundation of China (Grant No. 2019A610088), the Open Subject of Key Laboratory of Embedded and Service Computing of Ministry of Education of China (Grant No. ESSCKF 2019-07), the Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application.

Author information

Authors and Affiliations

  1. Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo, China

    Yuan Zheke, Niu Jun, Lu Xurong & Yang Fangmeng

  2. Zhejiang Key Laboratory of Mobile Network Application Technology, Ningbo, China

    Niu Jun

  3. Zhejiang Engineering Research Center of Advcanced Mass Spectrometry and Clinical Application, Ningbo, China

    Niu Jun

Authors
  1. Yuan Zheke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Niu Jun
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Lu Xurong
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yang Fangmeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YZ and NJ wrote the main manuscript text. LX prepared Figs. 1–4 and YF prepared Figs. 5–8. YZ prepared Figs. 9–13 and Tables 1 and 2. All authors reviewed the manuscript.

Corresponding author

Correspondence to Niu Jun.

Ethics declarations

Conflict of interest

I declare that the authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

Ethics approval

Not applicable.

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

Zheke, Y., Jun, N., Xurong, L. et al. Identify spatio-temporal properties of network traffic by model checking. J Supercomput 79, 18886–18909 (2023). https://doi.org/10.1007/s11227-023-05388-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-023-05388-9

Keywords

Search

Navigation