Bayesian Classifier model for reactive routing improvement in mobile ad-hoc networks

Tran The Son; Hoa Le Minh; Graham Sexton; Nauman Aslam

doi:10.1109/WTS.2012.6266124

Bayesian Classifier model for reactive routing improvement in mobile ad-hoc networks

Tran The Son, Hoa Le Minh, Graham Sexton, Nauman Aslam

Computer Information Sciences Department

Research output: Contribution to conference › Paper › peer-review

1 Citation (Scopus)

Abstract

This paper introduces a Bayesian Classifier model to improve the performance for reactive routing protocols in mobile ad-hoc networks (MANETs). The model can help to increase the network throughput and reduce end-to-end delay by controlling the broadcast area. Additional information related to hop-counts and node densities are used to support routing protocol in broadcasting. It is shown that the achieved throughput is increased twice and the delay is reduced by 50% in comparison to existing conventional routing protocols. The number of control packets disseminated during route discovery process decreased significantly in comparison with conventional scheme.

Original language	English
DOIs	https://doi.org/10.1109/WTS.2012.6266124
Publication status	Published - 2012
Event	Wireless Telecommunications Symposium (WTS), 2012 - London Duration: 1 Jan 2012 → …

Conference

Conference	Wireless Telecommunications Symposium (WTS), 2012
Period	1/01/12 → …

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10.1109/WTS.2012.6266124

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title = "Bayesian Classifier model for reactive routing improvement in mobile ad-hoc networks",

abstract = "This paper introduces a Bayesian Classifier model to improve the performance for reactive routing protocols in mobile ad-hoc networks (MANETs). The model can help to increase the network throughput and reduce end-to-end delay by controlling the broadcast area. Additional information related to hop-counts and node densities are used to support routing protocol in broadcasting. It is shown that the achieved throughput is increased twice and the delay is reduced by 50% in comparison to existing conventional routing protocols. The number of control packets disseminated during route discovery process decreased significantly in comparison with conventional scheme.",

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AU - Sexton, Graham

AU - Aslam, Nauman

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N2 - This paper introduces a Bayesian Classifier model to improve the performance for reactive routing protocols in mobile ad-hoc networks (MANETs). The model can help to increase the network throughput and reduce end-to-end delay by controlling the broadcast area. Additional information related to hop-counts and node densities are used to support routing protocol in broadcasting. It is shown that the achieved throughput is increased twice and the delay is reduced by 50% in comparison to existing conventional routing protocols. The number of control packets disseminated during route discovery process decreased significantly in comparison with conventional scheme.

AB - This paper introduces a Bayesian Classifier model to improve the performance for reactive routing protocols in mobile ad-hoc networks (MANETs). The model can help to increase the network throughput and reduce end-to-end delay by controlling the broadcast area. Additional information related to hop-counts and node densities are used to support routing protocol in broadcasting. It is shown that the achieved throughput is increased twice and the delay is reduced by 50% in comparison to existing conventional routing protocols. The number of control packets disseminated during route discovery process decreased significantly in comparison with conventional scheme.

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DO - 10.1109/WTS.2012.6266124

M3 - Paper

T2 - Wireless Telecommunications Symposium (WTS), 2012

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ER -

Bayesian Classifier model for reactive routing improvement in mobile ad-hoc networks

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