Article

A Multiobjective Strategy to Allocate Roadside Units in a Vehicular Network with Guaranteed Levels of Service

Authors:

Flávio Vinícius Martins,

João F. Sarubbi,

Elizabeth F. WannerAuthors Info & Claims

EMO 2017: 9th International Conference on Evolutionary Multi-Criterion Optimization - Volume 10173

Pages 120 - 134

https://doi.org/10.1007/978-3-319-54157-0_9

Published: 19 March 2017 Publication History

Abstract

In this work, we propose the Delta-MGA, a specific multiobjective algorithm for solving the allocation of Roadside Units RSUs in a Vehicular Network VANETs. We propose two multiobjective models to solve two different problems. The first one, our objectives are to find the minimum set of RSUs and to maximize the number of covered vehicles. The second one, our objectives are to find the minimum set of RSUs and to maximize the percentage of time that each vehicle remains connected. Our metric is based on Delta Network metric proposed in literature. As far as we concerned, Delta-MGA is the first multiobjective approach to present a deployment strategy for VANETs. We compare our approach with two mono-objective algorithms: i Delta-r; ii Delta-GA. Our results demonstrate that our approach gets better results when compared with Delta-r algorithm and competitive results when compared with Delta-GA algorithm. Furthermore, the main advantage of Delta-MGA algorithm is that with it is possible to find several different solutions given to the planning authorities diverse alternatives to deploy the RSUs.

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Cited By

Ma TChen H(2021)Short-term Traffic Prediction Based on Genetic Improved Wavelet Neural Network2021 International Symposium on Electrical, Electronics and Information Engineering10.1145/3459104.3459183(477-482)Online publication date: 19-Feb-2021
https://dl.acm.org/doi/10.1145/3459104.3459183

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Information

Published In

cover image Guide Proceedings

EMO 2017: 9th International Conference on Evolutionary Multi-Criterion Optimization - Volume 10173

March 2017

700 pages

ISBN:9783319541563

Editors:
Heike Trautmann
University of Münster, Münster, Germany
,
Günter Rudolph
TU Dortmund University, Dortmund, Germany
,
Kathrin Klamroth
University of Wuppertal, Wuppertal, Germany
,
Oliver Schütze
CINVESTAV-IPN, Mexico City, Mexico
,
Margaret Wiecek
Clemson University, Clemson, USA
,
Yaochu Jin
University of Surrey, Guildford, United Kingdom
,
Christian Grimme
University of Münster, Münster, Germany

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 19 March 2017

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Cited By

Ma TChen H(2021)Short-term Traffic Prediction Based on Genetic Improved Wavelet Neural Network2021 International Symposium on Electrical, Electronics and Information Engineering10.1145/3459104.3459183(477-482)Online publication date: 19-Feb-2021
https://dl.acm.org/doi/10.1145/3459104.3459183

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