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A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks

The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO) algorithm to predict...

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Main Authors: Goudarzi, S., Hassan, W.H., Anisi, M.H., Soleymani, S.A., Shabanzadeh, P.
Format: Article
Language:English
Published: Hindawi Publishing Corporation 2015
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.um.edu.my/17525/1/GoudarziS_%282015%29.pdf
http://eprints.um.edu.my/17525/
http://dx.doi.org/10.1155/2015/620658
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spelling my.um.eprints.175252017-07-17T04:36:55Z http://eprints.um.edu.my/17525/ A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks Goudarzi, S. Hassan, W.H. Anisi, M.H. Soleymani, S.A. Shabanzadeh, P. QA75 Electronic computers. Computer science The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO) algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO) and the RBF neural networks. The results of the proposed methodology compare the predictive capabilities in terms of coefficient determination (R2) and mean square error (MSE) based on the validation dataset. The results show that the effect of the model based on the CF-PSO is better than that of the model based on the RBF neural network in predicting the received signal strength indicator situation. In addition, we present a novel network selection algorithm to select the best candidate access point among the various access technologies based on the PSO. Simulation results indicate that using CF-PSO algorithm can decrease the number of unnecessary handovers and prevent the “Ping-Pong” effect. Moreover, it is demonstrated that the multiobjective particle swarm optimization based method finds an optimal network selection in a heterogeneous wireless environment. Hindawi Publishing Corporation 2015 Article PeerReviewed application/pdf en http://eprints.um.edu.my/17525/1/GoudarziS_%282015%29.pdf Goudarzi, S. and Hassan, W.H. and Anisi, M.H. and Soleymani, S.A. and Shabanzadeh, P. (2015) A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks. Mathematical Problems in Engineering, 2015. pp. 1-16. ISSN 1024-123X http://dx.doi.org/10.1155/2015/620658 doi:10.1155/2015/620658
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Goudarzi, S.
Hassan, W.H.
Anisi, M.H.
Soleymani, S.A.
Shabanzadeh, P.
A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks
description The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO) algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO) and the RBF neural networks. The results of the proposed methodology compare the predictive capabilities in terms of coefficient determination (R2) and mean square error (MSE) based on the validation dataset. The results show that the effect of the model based on the CF-PSO is better than that of the model based on the RBF neural network in predicting the received signal strength indicator situation. In addition, we present a novel network selection algorithm to select the best candidate access point among the various access technologies based on the PSO. Simulation results indicate that using CF-PSO algorithm can decrease the number of unnecessary handovers and prevent the “Ping-Pong” effect. Moreover, it is demonstrated that the multiobjective particle swarm optimization based method finds an optimal network selection in a heterogeneous wireless environment.
format Article
author Goudarzi, S.
Hassan, W.H.
Anisi, M.H.
Soleymani, S.A.
Shabanzadeh, P.
author_facet Goudarzi, S.
Hassan, W.H.
Anisi, M.H.
Soleymani, S.A.
Shabanzadeh, P.
author_sort Goudarzi, S.
title A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks
title_short A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks
title_full A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks
title_fullStr A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks
title_full_unstemmed A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks
title_sort novel model on curve fitting and particle swarm optimization for vertical handover in heterogeneous wireless networks
publisher Hindawi Publishing Corporation
publishDate 2015
url http://eprints.um.edu.my/17525/1/GoudarziS_%282015%29.pdf
http://eprints.um.edu.my/17525/
http://dx.doi.org/10.1155/2015/620658
_version_ 1643690442895654912
score 13.251813

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