Um protocolo de disseminação de dados adaptativo para redes veiculares
Ano de defesa: | 2014 |
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Autor(a) principal: | |
Orientador(a): | |
Banca de defesa: | |
Tipo de documento: | Dissertação |
Tipo de acesso: | Acesso aberto |
Idioma: | por |
Instituição de defesa: |
Universidade Federal de Minas Gerais
UFMG |
Programa de Pós-Graduação: |
Não Informado pela instituição
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Departamento: |
Não Informado pela instituição
|
País: |
Não Informado pela instituição
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Palavras-chave em Português: | |
Link de acesso: | http://hdl.handle.net/1843/ESBF-9KKQTM |
Resumo: | Data dissemination protocols for vehicular networks take into account the network topology to create connected dissemination paths. However, the network topology changes constantly due to the dynamic behavior of vehicular scenarios, leading to increases in overhead rate, delivery delay and packet losses. Aiming at developing a protocol easily adaptable to dynamic vehicular scenarios, we propose TODD an adaptive Traffic-Oriented Data Dissemination protocol. It uses real-time traffic information to dynamically choose the best relay vehicles. This process is done by analyzing a metric that is computed for each candidate vehicle, which may emphasize to specific vehicle characteristics, such as speed and distance to destination, based on the current traffic information. In addition, a centralized version of TODD (CTODD) is proposed to deal with the lack of real-time traffic information stored in the vehicles. In CTODD, fixed stations located in intersections are responsible for gathering and analyzing vehicle traffic information and disseminating data packets. Simulations scenarios were based on a grid and the downtown of Belo Horizonte, varying vehicle density. The results show that the proposed protocols can successfully deliver up to 55% more packets, generating up to 97% less overhead caused by control packets, compared to other important state-of-art protocols. Moreover, the proposed protocols showed, on average, lower delivery delay compared to specific protocols in both evaluated scenarios. |