Análise da topologia de redes veiculares usando métricas de centralidade

• Ano de defesa: 2019
• Autor(a) principal: Oliveira, Igor Marcos Araujo de
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Alagoas; Brasil; Programa de Pós-Graduação em Modelagem Computacional de Conhecimento; UFAL
• Palavras-chave em Português: Redes Complexas; Hellinger; Redes Veiculares; VANETs; Resiliência; Trafego; Vulnerabilidades; Complex Networks; Vehicular Networks; Resilience; Traffic; Vulnerabilities; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://www.repositorio.ufal.br/handle/riufal/6674

Vehicle networks are called VANET, in VANETs data propagation is one of the central themes in the search for efficiency in data transmission. Studies are being conducted to improve the quality of data transmission between vehicles, to investigate vehicle behavior on the road at certain time periods and to predict the best or shortest route, always with the aim of propagating data and strategizing the case. of connection failures. Thus the connection between vehicles is a barrier that can make the network objective impossible, preventing data from reachingits destinationin time to avoid,for example, a collision between automatavehicles. This paper seeks to analyze the topology of vehicular networks in the cities of Cologne, Germany, Madrid and Créteil in France, mapping them as complex networks for each collection time and extracting metric properties from graph theory and complex network ssuch as: Degree Medium, Clustering Coefficient, Betweenness, Closeness, Diameter. The datas set is then applied to Hellinger’s stochastic quantifier to evaluate changes between collection time points. In this sense, the evaluation indicates possible divergences between time periods that may justify potential problems in the network path, as well as the need for structural improvements. This work was based on the methodology that consisted of the followingsteps: I)network definition,II)network treatment,III)network characteristics treatment, IV) visual inspection. The results were numerically and graphically generated to allow the evaluation and behavioral interpretation of the studied networks based on the centrality measures and the stochastic quantifier used in this work