Allocating Roadside Units in VANETs Using a Variable Neighborhood Search Strategy

Joao F.M. Sarubbi; Tais R. Silva; Flavio V.C. Martins; Elizabeth F. Wanner; Cristiano M. Silva

doi:10.1109/VTCSpring.2017.8108447

Allocating Roadside Units in VANETs Using a Variable Neighborhood Search Strategy

Joao F.M. Sarubbi, Tais R. Silva, Flavio V.C. Martins, Elizabeth F. Wanner, Cristiano M. Silva

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

Abstract

In this work, we propose a GRASP+VNS algorithm for solving the allocation of Roadside Units (RSUs) in a Vehicular Network. Our main objective is to find the minimum set of RSUs to meet a Deployment Delta (ρ1,ρ2). The Deployment Delta (ρ1,ρ2) is a metric for specifying minimal communication guarantees from the infrastructure supporting the Vehicular Network. We compare GRASP+VNS to some baseline algorithms: (i) Delta-g; (ii) Delta-r and, (iii) the optimal value. Our results demonstrate that our approach requires up to 90% less Roadside Units to meet the QoS required by Deployment Delta (ρ1,ρ2) metric. Besides, different from the baseline algorithms, our approach find results that differ no more than 17% from the optimal values for all tested instances.

Original language	English
Title of host publication	2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings
Publisher	IEEE
Volume	2017-June
ISBN (Electronic)	9781509059324
DOIs	https://doi.org/10.1109/VTCSpring.2017.8108447
Publication status	Published - 14 Nov 2017
Event	85th IEEE Vehicular Technology Conference, VTC Spring 2017 - Sydney, Australia Duration: 4 Jun 2017 → 7 Jun 2017

Conference

Conference	85th IEEE Vehicular Technology Conference, VTC Spring 2017
Country/Territory	Australia
City	Sydney
Period	4/06/17 → 7/06/17

Access to Document

10.1109/VTCSpring.2017.8108447

Cite this

@inproceedings{99032a69f49e45b28959c5b7765409df,

title = "Allocating Roadside Units in VANETs Using a Variable Neighborhood Search Strategy",

abstract = "In this work, we propose a GRASP+VNS algorithm for solving the allocation of Roadside Units (RSUs) in a Vehicular Network. Our main objective is to find the minimum set of RSUs to meet a Deployment Delta (ρ1,ρ2). The Deployment Delta (ρ1,ρ2) is a metric for specifying minimal communication guarantees from the infrastructure supporting the Vehicular Network. We compare GRASP+VNS to some baseline algorithms: (i) Delta-g; (ii) Delta-r and, (iii) the optimal value. Our results demonstrate that our approach requires up to 90% less Roadside Units to meet the QoS required by Deployment Delta (ρ1,ρ2) metric. Besides, different from the baseline algorithms, our approach find results that differ no more than 17% from the optimal values for all tested instances.",

author = "Sarubbi, {Joao F.M.} and Silva, {Tais R.} and Martins, {Flavio V.C.} and Wanner, {Elizabeth F.} and Silva, {Cristiano M.}",

year = "2017",

month = nov,

day = "14",

doi = "10.1109/VTCSpring.2017.8108447",

language = "English",

volume = "2017-June",

booktitle = "2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings",

publisher = "IEEE",

address = "United States",

note = "85th IEEE Vehicular Technology Conference, VTC Spring 2017 ; Conference date: 04-06-2017 Through 07-06-2017",

}

Sarubbi, JFM, Silva, TR, Martins, FVC, Wanner, EF & Silva, CM 2017, Allocating Roadside Units in VANETs Using a Variable Neighborhood Search Strategy. in 2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings. vol. 2017-June, 8108447, IEEE, 85th IEEE Vehicular Technology Conference, VTC Spring 2017, Sydney, Australia, 4/06/17. https://doi.org/10.1109/VTCSpring.2017.8108447

TY - GEN

T1 - Allocating Roadside Units in VANETs Using a Variable Neighborhood Search Strategy

AU - Sarubbi, Joao F.M.

AU - Silva, Tais R.

AU - Martins, Flavio V.C.

AU - Wanner, Elizabeth F.

AU - Silva, Cristiano M.

PY - 2017/11/14

Y1 - 2017/11/14

N2 - In this work, we propose a GRASP+VNS algorithm for solving the allocation of Roadside Units (RSUs) in a Vehicular Network. Our main objective is to find the minimum set of RSUs to meet a Deployment Delta (ρ1,ρ2). The Deployment Delta (ρ1,ρ2) is a metric for specifying minimal communication guarantees from the infrastructure supporting the Vehicular Network. We compare GRASP+VNS to some baseline algorithms: (i) Delta-g; (ii) Delta-r and, (iii) the optimal value. Our results demonstrate that our approach requires up to 90% less Roadside Units to meet the QoS required by Deployment Delta (ρ1,ρ2) metric. Besides, different from the baseline algorithms, our approach find results that differ no more than 17% from the optimal values for all tested instances.

AB - In this work, we propose a GRASP+VNS algorithm for solving the allocation of Roadside Units (RSUs) in a Vehicular Network. Our main objective is to find the minimum set of RSUs to meet a Deployment Delta (ρ1,ρ2). The Deployment Delta (ρ1,ρ2) is a metric for specifying minimal communication guarantees from the infrastructure supporting the Vehicular Network. We compare GRASP+VNS to some baseline algorithms: (i) Delta-g; (ii) Delta-r and, (iii) the optimal value. Our results demonstrate that our approach requires up to 90% less Roadside Units to meet the QoS required by Deployment Delta (ρ1,ρ2) metric. Besides, different from the baseline algorithms, our approach find results that differ no more than 17% from the optimal values for all tested instances.

UR - http://www.scopus.com/inward/record.url?scp=85040543915&partnerID=8YFLogxK

UR - https://ieeexplore.ieee.org/document/8108447/

U2 - 10.1109/VTCSpring.2017.8108447

DO - 10.1109/VTCSpring.2017.8108447

M3 - Conference publication

AN - SCOPUS:85040543915

VL - 2017-June

BT - 2017 IEEE 85th Vehicular Technology Conference, VTC Spring 2017 - Proceedings

PB - IEEE

T2 - 85th IEEE Vehicular Technology Conference, VTC Spring 2017

Y2 - 4 June 2017 through 7 June 2017

ER -

Allocating Roadside Units in VANETs Using a Variable Neighborhood Search Strategy

Abstract

Conference

Access to Document

Other files and links

Fingerprint

Cite this