Combining Lyapunov Optimization With Actor-Critic Networks for Privacy-Aware IIoT Computation Offloading

Wu, G; Chen, X; Shen, Y; Xu, Z; Zhang, H; Shen, S; Yu, S

Combining Lyapunov Optimization With Actor-Critic Networks for Privacy-Aware IIoT Computation Offloading

Wu, G Chen, X Shen, Y Xu, Z Zhang, H Shen, S Yu, S

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Internet of Things Journal, 2024, 11, (10), pp. 17437-17452
Issue Date:: 2024-05-15

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Field	Value	Language
dc.contributor.author	Wu, G
dc.contributor.author	Chen, X
dc.contributor.author	Shen, Y
dc.contributor.author	Xu, Z
dc.contributor.author	Zhang, H
dc.contributor.author	Shen, S
dc.contributor.author	Yu, S https://orcid.org/0000-0003-4485-6743
dc.date.accessioned	2024-08-07T04:44:31Z
dc.date.available	2024-08-07T04:44:31Z
dc.date.issued	2024-05-15
dc.identifier.citation	IEEE Internet of Things Journal, 2024, 11, (10), pp. 17437-17452
dc.identifier.issn	2327-4662
dc.identifier.issn	2327-4662
dc.identifier.uri	http://hdl.handle.net/10453/180226
dc.description.abstract	Opportunistic computation offloading is an effective way to improve the computing performance of Industrial Internet of Things (IIoT) devices. However, as more and more computing tasks are being offloaded to mobile-edge computing (MEC) servers for processing, it can lead to IIoT privacy and security issues, such as personal usage habits. In this article, we aim to design a Lyapunov-based privacy-aware framework that defines the amount of IIoT user privacy and designs a 'reduced amount of privacy' mechanism. We first define the cumulative privacy amount for each IIoT user and trigger the privacy protection mechanism when the cumulative privacy amount exceeds the set privacy threshold. The offloading data generated by the IIoT user is then transferred to local processing, and finally, the cumulative privacy amount of the IIoT user is reduced. This model ensures that the cumulative privacy of all IIoT users remains stable. We further combine the advantages of Lyapunov optimization and actor-critic networks to address the problem of how to make the model learn the optimal policy and maintain the minimum energy consumption in the long run. Especially, this framework integrates model-based optimization and model-free actor-critic networks to handle the offloading problem with very low-computational complexity, and Lyapunov optimization ensures that this framework minimizes energy consumption while stabilizing the data queue. It is demonstrated through experimental simulation results that the proposed scheme can maintain data queue stability and minimize energy consumption under strict security.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Internet of Things Journal
dc.relation.isbasedon	10.1109/JIOT.2024.3357110
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0805 Distributed Computing, 1005 Communications Technologies
dc.subject.classification	40 Engineering
dc.subject.classification	46 Information and computing sciences
dc.title	Combining Lyapunov Optimization With Actor-Critic Networks for Privacy-Aware IIoT Computation Offloading
dc.type	Journal Article
utslib.citation.volume	11
utslib.for	0805 Distributed Computing
utslib.for	1005 Communications Technologies
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/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/Strength - CCSP - Centre for Cyber Security and Privacy
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-07T04:44:25Z
pubs.issue	10
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	10

Abstract:

Opportunistic computation offloading is an effective way to improve the computing performance of Industrial Internet of Things (IIoT) devices. However, as more and more computing tasks are being offloaded to mobile-edge computing (MEC) servers for processing, it can lead to IIoT privacy and security issues, such as personal usage habits. In this article, we aim to design a Lyapunov-based privacy-aware framework that defines the amount of IIoT user privacy and designs a 'reduced amount of privacy' mechanism. We first define the cumulative privacy amount for each IIoT user and trigger the privacy protection mechanism when the cumulative privacy amount exceeds the set privacy threshold. The offloading data generated by the IIoT user is then transferred to local processing, and finally, the cumulative privacy amount of the IIoT user is reduced. This model ensures that the cumulative privacy of all IIoT users remains stable. We further combine the advantages of Lyapunov optimization and actor-critic networks to address the problem of how to make the model learn the optimal policy and maintain the minimum energy consumption in the long run. Especially, this framework integrates model-based optimization and model-free actor-critic networks to handle the offloading problem with very low-computational complexity, and Lyapunov optimization ensures that this framework minimizes energy consumption while stabilizing the data queue. It is demonstrated through experimental simulation results that the proposed scheme can maintain data queue stability and minimize energy consumption under strict security.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/180226