A multiobjective strategy to allocate roadside units in a vehicular network with guaranteed levels of service

Flávio Vinícius Cruzeiro Martins; João F.M. Sarubbi; Elizabeth F. Wanner

doi:10.1007/978-3-319-54157-0_9

A multiobjective strategy to allocate roadside units in a vehicular network with guaranteed levels of service

Flávio Vinícius Cruzeiro Martins^*, João F.M. Sarubbi, Elizabeth F. Wanner

^*Corresponding author for this work

College of Engineering and Physical Sciences

Research output: Chapter in Book/Published conference output › Conference publication

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.

Original language	English
Title of host publication	Evolutionary Multi-Criterion Optimization
Subtitle of host publication	9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings
Editors	Heike Trautmann, Rudolph Günter, et al
Place of Publication	Cham (CH)
Publisher	Springer
Pages	120-134
Number of pages	15
ISBN (Electronic)	978-3-319-54157-0
ISBN (Print)	978-3-319-54156-3
DOIs	https://doi.org/10.1007/978-3-319-54157-0_9
Publication status	Published - 2017
Event	9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 - Munster, Germany Duration: 19 Mar 2017 → 22 Mar 2017

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	10173
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017
Country/Territory	Germany
City	Munster
Period	19/03/17 → 22/03/17

Keywords

quality of service
roadside unit deployment
vehicular network

Access to Document

10.1007/978-3-319-54157-0_9

Cite this

Martins, F. V. C., Sarubbi, J. F. M., & Wanner, E. F. (2017). A multiobjective strategy to allocate roadside units in a vehicular network with guaranteed levels of service. In H. Trautmann, R. Günter, & et al (Eds.), Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings (pp. 120-134). (Lecture Notes in Computer Science; Vol. 10173). Springer. https://doi.org/10.1007/978-3-319-54157-0_9

Martins, Flávio Vinícius Cruzeiro ; Sarubbi, João F.M. ; Wanner, Elizabeth F. / A multiobjective strategy to allocate roadside units in a vehicular network with guaranteed levels of service. Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings. editor / Heike Trautmann ; Rudolph Günter ; et al. Cham (CH) : Springer, 2017. pp. 120-134 (Lecture Notes in Computer Science).

@inproceedings{7dc16420cb1d40999d99c4a6e7bf7ca4,

title = "A multiobjective strategy to allocate roadside units in a vehicular network with guaranteed levels of service",

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.",

keywords = "quality of service, roadside unit deployment, vehicular network",

author = "Martins, {Fl{\'a}vio Vin{\'i}cius Cruzeiro} and Sarubbi, {Jo{\~a}o F.M.} and Wanner, {Elizabeth F.}",

year = "2017",

doi = "10.1007/978-3-319-54157-0_9",

language = "English",

isbn = "978-3-319-54156-3",

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editor = "Heike Trautmann and Rudolph G{\"u}nter and {et al}",

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note = "9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017 ; Conference date: 19-03-2017 Through 22-03-2017",

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Martins, FVC, Sarubbi, JFM & Wanner, EF 2017, A multiobjective strategy to allocate roadside units in a vehicular network with guaranteed levels of service. in H Trautmann, R Günter & et al (eds), Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings. Lecture Notes in Computer Science, vol. 10173, Springer, Cham (CH), pp. 120-134, 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017, Munster, Germany, 19/03/17. https://doi.org/10.1007/978-3-319-54157-0_9

A multiobjective strategy to allocate roadside units in a vehicular network with guaranteed levels of service. / Martins, Flávio Vinícius Cruzeiro; Sarubbi, João F.M.; Wanner, Elizabeth F.
Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings. ed. / Heike Trautmann; Rudolph Günter; et al. Cham (CH): Springer, 2017. p. 120-134 (Lecture Notes in Computer Science; Vol. 10173).

Research output: Chapter in Book/Published conference output › Conference publication

TY - GEN

T1 - A multiobjective strategy to allocate roadside units in a vehicular network with guaranteed levels of service

AU - Martins, Flávio Vinícius Cruzeiro

AU - Sarubbi, João F.M.

AU - Wanner, Elizabeth F.

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

KW - quality of service

KW - roadside unit deployment

KW - vehicular network

UR - http://link.springer.com/chapter/10.1007%2F978-3-319-54157-0_9

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M3 - Conference publication

AN - SCOPUS:85014241817

SN - 978-3-319-54156-3

T3 - Lecture Notes in Computer Science

SP - 120

EP - 134

BT - Evolutionary Multi-Criterion Optimization

A2 - Trautmann, Heike

A2 - Günter, Rudolph

A2 - et al,

PB - Springer

CY - Cham (CH)

T2 - 9th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2017

Y2 - 19 March 2017 through 22 March 2017

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Martins FVC, Sarubbi JFM, Wanner EF. A multiobjective strategy to allocate roadside units in a vehicular network with guaranteed levels of service. In Trautmann H, Günter R, et al, editors, Evolutionary Multi-Criterion Optimization: 9th International Conference, EMO 2017, Münster, Germany, March 19-22, 2017, Proceedings. Cham (CH): Springer. 2017. p. 120-134. (Lecture Notes in Computer Science). Epub 2017 Feb 19. doi: 10.1007/978-3-319-54157-0_9

A multiobjective strategy to allocate roadside units in a vehicular network with guaranteed levels of service

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Keywords

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Dimensionality reduction approach for many-objective vehicle routing problem with demand responsive transport

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