Optimizing Broadcasting Scheme for VANETs Using Genetic Algorithm

Research output: Book chapter/Published conference paperConference paper

4 Citations (Scopus)

Abstract

Broadcasting is one of the communication mechanism utilised in VANET architecture through which an up-to-date traffic data can be disseminated among the commuters and this can help reduce traffic jams/congestions. Broadcasting storm (broadcasting) is considered to be an NP-hard problem consisting of multiple objectives. Conventional techniques use Multi-Objective Genetic Algorithms (MOGAs) to solve such optimization problems. Performance of such algorithms depend on fitness function. In this paper, we propose a novel and improved fitness function for MOGA to solve the broadcasting problem in VANETs. The proposed fitness function has enhanced the rate of evolution, resulting in more generations and producing better optimization results. We consider a highway scenario for simulation to evaluate performance of the proposed solutions. We compare the results of the proposed algorithm with existing state-of-the-art technique [1]. Our results show improvement in reduction of the propagation time and the number of retransmissions compared to the previous solution.
Original languageEnglish
Title of host publicationProceedings of the 41st Annual IEEE Conference on Local Computer Networks (LCN 2016)
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages222-229
Number of pages8
ISBN (Electronic)9781509023479
ISBN (Print)9781509023486
DOIs
Publication statusPublished - 16 Feb 2017
Event2016 41st IEEE Conference on Local Computer Networks: LCN Workshops 2016 - The Address Dubai Mall, Dubai, United Arab Emirates
Duration: 07 Nov 201610 Nov 2016
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7795840 (Conference proceedings)
https://www.ieeelcn.org/prior/LCN41/index.html (Conference website)

Conference

Conference2016 41st IEEE Conference on Local Computer Networks
CountryUnited Arab Emirates
CityDubai
Period07/11/1610/11/16
OtherThe IEEE LCN conference is the premier conference on theoretical and practical aspects of computer networking. LCN is highly interactive, enabling an effective interchange of results and ideas among researchers, users, and product developers. Major developments from high-speed networks to the global Internet to specialized sensor networks have been reported at past LCNs.
Internet address

Fingerprint

Broadcasting
Genetic algorithms
Traffic congestion
Computational complexity
Communication

Cite this

Jafer, M., Khan, M., Rehman, S-U., & Zia, T. (2017). Optimizing Broadcasting Scheme for VANETs Using Genetic Algorithm. In Proceedings of the 41st Annual IEEE Conference on Local Computer Networks (LCN 2016) (pp. 222-229). [7856160] United States: IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/LCN.2016.051
Jafer, Muhammad ; Khan, Muhammad ; Rehman, Sabih-Ur ; Zia, Tanveer. / Optimizing Broadcasting Scheme for VANETs Using Genetic Algorithm. Proceedings of the 41st Annual IEEE Conference on Local Computer Networks (LCN 2016). United States : IEEE, Institute of Electrical and Electronics Engineers, 2017. pp. 222-229
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title = "Optimizing Broadcasting Scheme for VANETs Using Genetic Algorithm",
abstract = "Broadcasting is one of the communication mechanism utilised in VANET architecture through which an up-to-date traffic data can be disseminated among the commuters and this can help reduce traffic jams/congestions. Broadcasting storm (broadcasting) is considered to be an NP-hard problem consisting of multiple objectives. Conventional techniques use Multi-Objective Genetic Algorithms (MOGAs) to solve such optimization problems. Performance of such algorithms depend on fitness function. In this paper, we propose a novel and improved fitness function for MOGA to solve the broadcasting problem in VANETs. The proposed fitness function has enhanced the rate of evolution, resulting in more generations and producing better optimization results. We consider a highway scenario for simulation to evaluate performance of the proposed solutions. We compare the results of the proposed algorithm with existing state-of-the-art technique [1]. Our results show improvement in reduction of the propagation time and the number of retransmissions compared to the previous solution.",
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Jafer, M, Khan, M, Rehman, S-U & Zia, T 2017, Optimizing Broadcasting Scheme for VANETs Using Genetic Algorithm. in Proceedings of the 41st Annual IEEE Conference on Local Computer Networks (LCN 2016)., 7856160, IEEE, Institute of Electrical and Electronics Engineers, United States, pp. 222-229, 2016 41st IEEE Conference on Local Computer Networks, Dubai, United Arab Emirates, 07/11/16. https://doi.org/10.1109/LCN.2016.051

Optimizing Broadcasting Scheme for VANETs Using Genetic Algorithm. / Jafer, Muhammad; Khan, Muhammad; Rehman, Sabih-Ur; Zia, Tanveer.

Proceedings of the 41st Annual IEEE Conference on Local Computer Networks (LCN 2016). United States : IEEE, Institute of Electrical and Electronics Engineers, 2017. p. 222-229 7856160.

Research output: Book chapter/Published conference paperConference paper

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Jafer M, Khan M, Rehman S-U, Zia T. Optimizing Broadcasting Scheme for VANETs Using Genetic Algorithm. In Proceedings of the 41st Annual IEEE Conference on Local Computer Networks (LCN 2016). United States: IEEE, Institute of Electrical and Electronics Engineers. 2017. p. 222-229. 7856160 https://doi.org/10.1109/LCN.2016.051