Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm

Rajawat, AS; Goyal, SB; Chauhan, C; Bedi, P; Prasad, M; Jan, T

Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm

Rajawat, AS Goyal, SB Chauhan, C Bedi, P Prasad, M

Jan, T

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Publisher:: MDPI
Publication Type:: Journal Article
Citation:: Electronics (Switzerland), 2023, 12, (1), pp. 217
Issue Date:: 2023-01-01

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Field	Value	Language
dc.contributor.author	Rajawat, AS
dc.contributor.author	Goyal, SB
dc.contributor.author	Chauhan, C
dc.contributor.author	Bedi, P
dc.contributor.author	Prasad, M https://orcid.org/0000-0002-7745-9667
dc.contributor.author	Jan, T
dc.date.accessioned	2023-03-24T09:32:34Z
dc.date.available	2023-03-24T09:32:34Z
dc.date.issued	2023-01-01
dc.identifier.citation	Electronics (Switzerland), 2023, 12, (1), pp. 217
dc.identifier.issn	1450-5843
dc.identifier.issn	2079-9292
dc.identifier.uri	http://hdl.handle.net/10453/168351
dc.description.abstract	Agile product development cycles and re-configurable Industrial Internet of Things (IIoT) allow more flexible and resilient industrial production systems that can handle a broader range of challenges and improve their productivity. Reinforcement Learning (RL) was shown to be able to support industrial production systems to be flexible and resilient to respond to changes in real time. This study examines the use of RL in a wide range of adaptive cognitive systems with IIoT-edges in manufacturing processes. We propose a cognitive adaptive system using IIoT with RL (CAS-IIoT-RL) and our experimental analysis showed that the proposed model showed improvements with adaptive and dynamic decision controls in challenging industrial environments.
dc.language	en
dc.publisher	MDPI
dc.relation.ispartof	Electronics (Switzerland)
dc.relation.isbasedon	10.3390/electronics12010217
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.title	Cognitive Adaptive Systems for Industrial Internet of Things Using Reinforcement Algorithm
dc.type	Journal Article
utslib.citation.volume	12
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
dc.date.updated	2023-03-24T09:32:33Z
pubs.issue	1
pubs.publication-status	Published
pubs.volume	12
utslib.citation.issue	1

Abstract:

Agile product development cycles and re-configurable Industrial Internet of Things (IIoT) allow more flexible and resilient industrial production systems that can handle a broader range of challenges and improve their productivity. Reinforcement Learning (RL) was shown to be able to support industrial production systems to be flexible and resilient to respond to changes in real time. This study examines the use of RL in a wide range of adaptive cognitive systems with IIoT-edges in manufacturing processes. We propose a cognitive adaptive system using IIoT with RL (CAS-IIoT-RL) and our experimental analysis showed that the proposed model showed improvements with adaptive and dynamic decision controls in challenging industrial environments.

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http://hdl.handle.net/10453/168351