Identify spatio-temporal properties of network traffic by model checking-Reference-Cited by-同舟云学术

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Identify spatio-temporal properties of network traffic by model checking

Published:2023-05-20 Issue:16 Volume:79 Page:18886-18909
ISSN:0920-8542
Container-title:The Journal of Supercomputing
language:en
Short-container-title:J Supercomput

Author:

Zheke Yuan,Jun Niu,Xurong Lu,Fangmeng Yang

Funder

Ningbo Natural Science Foundation of China

the Open Subject of Key Laboratory of Embedded and Service Computing of Ministry of Education of China

Publisher

Springer Science and Business Media LLC

Subject

Hardware and Architecture,Information Systems,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s11227-023-05388-9.pdf

Reference40 articles.

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

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

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

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

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