Deep Reinforcement Learning Based Active Queue Management for IoT Networks-Reference-Cited by-同舟云学术

Deep Reinforcement Learning Based Active Queue Management for IoT Networks

Published:2021-04-30 Issue:3 Volume:29 Page:
ISSN:1064-7570
Container-title:Journal of Network and Systems Management
language:en
Short-container-title:J Netw Syst Manage

Author:

Kim Minsu,Jaseemuddin Muhammad,Anpalagan Alagan^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Strategy and Management,Computer Networks and Communications,Hardware and Architecture,Information Systems

Link

https://link.springer.com/content/pdf/10.1007/s10922-021-09603-x.pdf

Reference39 articles.

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2. tiny-dnn. https://github.com/tiny-dnn/tiny-dnn. Accessed 06 Feb 2021

3. Al-Kashoash, H.A.A., Amer, H.M., Mihaylova, L., et al.: Optimization-based hybrid congestion alleviation for 6LoWPAN networks. IEEE Internet Things J. 4, 2070–2081 (2017)

4. Amar, Y., Haddadi, H., Mortier, R., et al.: An Analysis of Home IoT Network Traffic and Behaviour. In: arXiv preprint arXiv:1803.05368 (2018)

5. Baker, F., Fairhurst, G.: IETF Recommendations Regarding Active Queue Management. In: Internet Engineering Task Force (IETF), RFC 7567 (2015)

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