Deep Reinforcement Learning for Smart City Communication Networks

Xia, Z; Xue, S; Wu, J; Chen, Y; Chen, J; Wu, L

Deep Reinforcement Learning for Smart City Communication Networks

Xia, Z Xue, S Wu, J Chen, Y Chen, J Wu, L

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Industrial Informatics, 2021, 17, (6), pp. 4188-4196
Issue Date:: 2021-06-01

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Field	Value	Language
dc.contributor.author	Xia, Z
dc.contributor.author	Xue, S
dc.contributor.author	Wu, J
dc.contributor.author	Chen, Y
dc.contributor.author	Chen, J
dc.contributor.author	Wu, L
dc.date.accessioned	2022-04-12T04:41:04Z
dc.date.available	2022-04-12T04:41:04Z
dc.date.issued	2021-06-01
dc.identifier.citation	IEEE Transactions on Industrial Informatics, 2021, 17, (6), pp. 4188-4196
dc.identifier.issn	1551-3203
dc.identifier.issn	1941-0050
dc.identifier.uri	http://hdl.handle.net/10453/156116
dc.description.abstract	Smart city communication networks hold the promise of harnessing the Internet of Things, smart devices, intelligent energy grids, autonomous cars, and many other data architectures to improve quality of life for every citizen. As time and progress have gradually shifted this concept from an idea into a reality, the possibilities for what services a smart city might provide have exploded. Along with that expansion, the number of devices that may need to be supported across a communications network has multiplied enormously. However, existing congestion control protocols are not equipped to support this higher requirement for network performance. New protocols are needed to provide higher data throughputs, reduce queuing delays and packet loss, and maintain stable and reliable communications pathways. The end-to-end congestion control protocol presented in this article does all these things. Called high throughput congestion control (HTCC), the framework takes full advantage of reinforcement learning and a fast growth algorithm to adaptively control the bytes in flight across the network's links to suit the prevailing network conditions. The result is a protocol that not only finds the optimal tradeoff between throughput, latency, and packet loss, but also significantly speeds up the learning process to avoid wasting network resources, use all free bandwidth and maximize network utilization. A comprehensive set of experiments with HTCC and six state-of-the-art protocols - Copa, PCC Vivace, PCC-Allegro, TCP Cubic, TaoVA-100x, and FillP-Sheep - demonstrate that, in most conditions, HTCC is able to make the best tradeoff between throughput, delay, and loss rate.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Industrial Informatics
dc.relation.isbasedon	10.1109/TII.2020.3006199
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Electrical & Electronic Engineering
dc.title	Deep Reinforcement Learning for Smart City Communication Networks
dc.type	Journal Article
utslib.citation.volume	17
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access	*
dc.date.updated	2022-04-12T04:41:02Z
pubs.issue	6
pubs.publication-status	Published
pubs.volume	17
utslib.citation.issue	6

Abstract:

Smart city communication networks hold the promise of harnessing the Internet of Things, smart devices, intelligent energy grids, autonomous cars, and many other data architectures to improve quality of life for every citizen. As time and progress have gradually shifted this concept from an idea into a reality, the possibilities for what services a smart city might provide have exploded. Along with that expansion, the number of devices that may need to be supported across a communications network has multiplied enormously. However, existing congestion control protocols are not equipped to support this higher requirement for network performance. New protocols are needed to provide higher data throughputs, reduce queuing delays and packet loss, and maintain stable and reliable communications pathways. The end-to-end congestion control protocol presented in this article does all these things. Called high throughput congestion control (HTCC), the framework takes full advantage of reinforcement learning and a fast growth algorithm to adaptively control the bytes in flight across the network's links to suit the prevailing network conditions. The result is a protocol that not only finds the optimal tradeoff between throughput, latency, and packet loss, but also significantly speeds up the learning process to avoid wasting network resources, use all free bandwidth and maximize network utilization. A comprehensive set of experiments with HTCC and six state-of-the-art protocols - Copa, PCC Vivace, PCC-Allegro, TCP Cubic, TaoVA-100x, and FillP-Sheep - demonstrate that, in most conditions, HTCC is able to make the best tradeoff between throughput, delay, and loss rate.

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http://hdl.handle.net/10453/156116