A hybrid-fuzzy logic guided genetic algorithm (H-FLGA) approach for resource optimization in 5G VANETs

Khan, AA; Abolhasan, M; Ni, W; Lipman, J; Jamalipour, A

A hybrid-fuzzy logic guided genetic algorithm (H-FLGA) approach for resource optimization in 5G VANETs

Khan, AA

Abolhasan, M

Ni, W

Lipman, J

Jamalipour, A

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Publication Type:: Journal Article
Citation:: IEEE Transactions on Vehicular Technology, 2019, 68 (7), pp. 6964 - 6974
Issue Date:: 2019-07-01

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Field	Value	Language
dc.contributor.author	Khan, AA https://orcid.org/0000-0001-6081-8160	en_US
dc.contributor.author	Abolhasan, M https://orcid.org/0000-0002-4282-6666	en_US
dc.contributor.author	Ni, W https://orcid.org/0000-0002-4933-594X	en_US
dc.contributor.author	Lipman, J https://orcid.org/0000-0003-2877-1168	en_US
dc.contributor.author	Jamalipour, A	en_US
dc.date.issued	2019-07-01	en_US
dc.identifier.citation	IEEE Transactions on Vehicular Technology, 2019, 68 (7), pp. 6964 - 6974	en_US
dc.identifier.issn	0018-9545	en_US
dc.identifier.uri	http://hdl.handle.net/10453/135633
dc.description.abstract	© 2019 IEEE. To support diversified quality of service demands and dynamic resource requirements of users in 5G driven VANETs, network resources need flexible and scalable resource allocation strategies. Current heterogeneous vehicular networks are designed and deployed with a connection-centric mindset with fixed resource allocation to a cell regardless of traffic conditions, static coverage, and capacity. In this paper, we propose a hybrid-fuzzy logic guided genetic algorithm (H-FLGA) approach for the software defined networking controller, to solve a multi-objective resource optimization problem for 5G driven VANETs. Realizing the service oriented view, the proposed approach formulates five different scenarios of network resource optimization in 5G VANETs. Furthermore, the proposed fuzzy inference system is used to optimize weights of multi-objectives, depending on the type of service requirements of customers. The proposed approach shows the minimized value of multi-objective cost function when compared with the GA. The simulation results show the minimized value of end-to-end delay as compared to other schemes. The proposed approach will help the network service providers to implement a customer-centric network infrastructure, depending on dynamic customer needs of users.	en_US
dc.relation.ispartof	IEEE Transactions on Vehicular Technology	en_US
dc.relation.isbasedon	10.1109/TVT.2019.2915194	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Automobile Design & Engineering	en_US
dc.title	A hybrid-fuzzy logic guided genetic algorithm (H-FLGA) approach for resource optimization in 5G VANETs	en_US
dc.type	Journal Article
utslib.citation.volume	7	en_US
utslib.citation.volume	68	en_US
utslib.for	0805 Distributed Computing	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
utslib.for	10 Technology	en_US
pubs.embargo.period	Not known	en_US
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
pubs.organisational-group	/University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
pubs.issue	7	en_US
pubs.publication-status	Published	en_US
pubs.volume	68	en_US

Abstract:

© 2019 IEEE. To support diversified quality of service demands and dynamic resource requirements of users in 5G driven VANETs, network resources need flexible and scalable resource allocation strategies. Current heterogeneous vehicular networks are designed and deployed with a connection-centric mindset with fixed resource allocation to a cell regardless of traffic conditions, static coverage, and capacity. In this paper, we propose a hybrid-fuzzy logic guided genetic algorithm (H-FLGA) approach for the software defined networking controller, to solve a multi-objective resource optimization problem for 5G driven VANETs. Realizing the service oriented view, the proposed approach formulates five different scenarios of network resource optimization in 5G VANETs. Furthermore, the proposed fuzzy inference system is used to optimize weights of multi-objectives, depending on the type of service requirements of customers. The proposed approach shows the minimized value of multi-objective cost function when compared with the GA. The simulation results show the minimized value of end-to-end delay as compared to other schemes. The proposed approach will help the network service providers to implement a customer-centric network infrastructure, depending on dynamic customer needs of users.

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

http://hdl.handle.net/10453/135633