Learning automata-based fault-tolerant system for dynamic autonomous unmanned vehicular networks

Misra, S; Venkata Krishna, P; Saritha, V; Agarwal, H; Vasilakos, AV; Obaidat, MS

Learning automata-based fault-tolerant system for dynamic autonomous unmanned vehicular networks

Misra, S Venkata Krishna, P Saritha, V Agarwal, H Vasilakos, AV Obaidat, MS

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Publisher:: Institute of Electrical and Electronics Engineers
Publication Type:: Journal Article
Citation:: IEEE Systems Journal, 2017, 11, (4), pp. 2929-2938
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Misra, S
dc.contributor.author	Venkata Krishna, P
dc.contributor.author	Saritha, V
dc.contributor.author	Agarwal, H
dc.contributor.author	Vasilakos, AV
dc.contributor.author	Obaidat, MS
dc.date.accessioned	2022-08-24T05:59:20Z
dc.date.available	2022-08-24T05:59:20Z
dc.date.issued	2017-01-01
dc.identifier.citation	IEEE Systems Journal, 2017, 11, (4), pp. 2929-2938
dc.identifier.issn	1932-8184
dc.identifier.issn	1937-9234
dc.identifier.uri	http://hdl.handle.net/10453/160799
dc.description.abstract	A fault-tolerant routing system is highly needed in autonomous unmanned vehicle (AUxV)-based networks because of the various constraints due to adversarial situations present in the environment of AUxVs. Critical consequences might be resulting even with a minor fault in the system software/hardware in these types of systems due to the involved hazard of their applications such as search and rescue, threat surveillance, and chemical and biohazard sampling. The architecture, capability, application, and power of the internal systems are the parameters that vary among the AUxV network member nodes. Fault-tolerant system design is a key aspect for AUxVs, and heterogeneity is to be considered while designing the fault-tolerant systems' AUxVs. This paper describes an approach to fault-tolerant AUxV networks by designing a routing method. The proposed algorithm for AUxVs is based on the cross-layer design and the learning automata (LA), referred as Unmanned Vehicle Network with LA-based Routing using Cross-Layer Design (ULARC). The optimal path between the source and the destination is obtained using the theory of LA. The effectiveness of the proposed strategy is proved by the proof of convergence and is shown in the Appendix.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers
dc.relation.ispartof	IEEE Systems Journal
dc.relation.isbasedon	10.1109/JSYST.2015.2418353
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Operations Research
dc.title	Learning automata-based fault-tolerant system for dynamic autonomous unmanned vehicular networks
dc.type	Journal Article
utslib.citation.volume	11
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-08-24T05:59:19Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	11
utslib.citation.issue	4

Abstract:

A fault-tolerant routing system is highly needed in autonomous unmanned vehicle (AUxV)-based networks because of the various constraints due to adversarial situations present in the environment of AUxVs. Critical consequences might be resulting even with a minor fault in the system software/hardware in these types of systems due to the involved hazard of their applications such as search and rescue, threat surveillance, and chemical and biohazard sampling. The architecture, capability, application, and power of the internal systems are the parameters that vary among the AUxV network member nodes. Fault-tolerant system design is a key aspect for AUxVs, and heterogeneity is to be considered while designing the fault-tolerant systems' AUxVs. This paper describes an approach to fault-tolerant AUxV networks by designing a routing method. The proposed algorithm for AUxVs is based on the cross-layer design and the learning automata (LA), referred as Unmanned Vehicle Network with LA-based Routing using Cross-Layer Design (ULARC). The optimal path between the source and the destination is obtained using the theory of LA. The effectiveness of the proposed strategy is proved by the proof of convergence and is shown in the Appendix.

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http://hdl.handle.net/10453/160799