Improving Roadside Unit deployment in vehicular networks by exploiting genetic algorithms

Fogue, M. ; Sanguesa, J.A. ; Martinez, F.J. ; Marquez-Barja, J.M.

Resumen: Vehicular networks make use of the Roadside Units (RSUs) to enhance the communication capabilities of the vehicles in order to forward control messages and/or to provide Internet access to vehicles, drivers and passengers. Unfortunately, within vehicular networks, the wireless signal propagation is mostly affected by buildings and other obstacles (e.g., urban fixtures), in particular when considering the IEEE 802.11p standard. Therefore, a crowded RSU deployment may be required to ensure vehicular communications within urban environments. Furthermore, some applications, notably those applications related to safety, require a fast and reliable warning data transmission to the emergency services and traffic authorities. However, communication is not always possible in vehicular environments due to the lack of connectivity even employing multiple hops. To overcome the signal propagation problem and delayed warning notification time issues, an effective, smart, cost-effective and all-purpose RSU deployment policy should be put into place. In this paper, we propose the genetic algorithm for roadside unit deployment (GARSUD) system, which uses a genetic algorithm that is capable of automatically providing an RSU deployment suitable for any given road map layout. Our simulation results show that GARSUD is able to reduce the warning notification time (the time required to inform emergency authorities in traffic danger situations) and to improve vehicular communication capabilities within different density scenarios and complexity layouts.

Idioma: Inglés

DOI: 10.3390/app8010086

Año: 2018

Publicado en: Applied Sciences (Switzerland) 8, 1 (2018), 010086 [21 pp]
ISSN: 2076-3417

Factor impacto JCR: 2.217 (2018)

Categ. JCR: PHYSICS, APPLIED rank: 66 / 148 = 0.446 (2018) - Q2 - T2
Categ. JCR: CHEMISTRY, MULTIDISCIPLINARY rank: 88 / 172 = 0.512 (2018) - Q3 - T2
Categ. JCR: MATERIALS SCIENCE, MULTIDISCIPLINARY rank: 151 / 293 = 0.515 (2018) - Q3 - T2

Factor impacto SCIMAGO: 0.379 - Computer Science Applications (Q1) - Engineering (miscellaneous) (Q1) - Process Chemistry and Technology (Q1) - Instrumentation (Q1) - Materials Science (miscellaneous) (Q1) - Fluid Flow and Transfer Processes (Q1)

Financiación: info:eu-repo/grantAgreement/ES/MINECO/TEC2014-52690-R

Tipo y forma: Artículo (Versión definitiva)

Área (Departamento): Lenguajes y Sistemas Informáticos (Departamento de Informática e Ingeniería de Sistemas)