An Infrastructureless Approach to Estimate Vehicular Density in Urban Environments

In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless c...

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Detalhes bibliográficos
Autores: Sanguesa, Julio A., Fogue, Manuel, Garrido, Piedad, Martinez, Francisco J, Cano, Juan-Carlos|||0000-0002-0038-0539, Tavares De Araujo Cesariny Calafate, Carlos Miguel|||0000-0001-5729-3041, Manzoni, Pietro|||0000-0003-3753-0403
Formato: artículo
Fecha de publicación:2013
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/28824
Acesso em linha:https://riunet.upv.es/handle/10251/28824
Access Level:acceso abierto
Palavra-chave:vehicular networks
ehicular density estimation
warning message dissemination
VANETs
ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES
Descrição
Resumo:In Vehicular Networks, communication success usually depends on the density of vehicles, since a higher density allows having shorter and more reliable wireless links. Thus, knowing the density of vehicles in a vehicular communications environment is important, as better opportunities for wireless communication can show up. However, vehicle density is highly variable in time and space. This paper deals with the importance of predicting the density of vehicles in vehicular environments to take decisions for enhancing the dissemination of warning messages between vehicles. We propose a novel mechanism to estimate the vehicular density in urban environments. Our mechanism uses as input parameters the number of beacons received per vehicle, and the topological characteristics of the environment where the vehicles are located. Simulation results indicate that, unlike previous proposals solely based on the number of beacons received, our approach is able to accurately estimate the vehicular density, and therefore it could support more efficient dissemination protocols for vehicular environments, as well as improve previously proposed schemes.