Análise da capacidade de descarregamento de dados usando o sistema de transporte público de uma metrópole

• Ano de defesa: 2018
• Autor(a) principal: Dias, Daniel de Souza
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Rio de Janeiro; Brasil; Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa de Engenharia; Programa de Pós-Graduação em Engenharia Elétrica; UFRJ
• Palavras-chave em Português: Engenharia elétrica; Redes veiculares; Dados de GPS dos ônibus da cidade do Rio de Janeiro; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA
• Link de acesso: http://hdl.handle.net/11422/9396

Smart cities will improve the quality of life of their citizens with the help of information and communication technologies. Among other tasks, it involves environmental monitoring and information analysis to determine appropriate actions. The accomplishment of these tasks depends on a reliable, ubiquitous communication infrastructure, such as a cellular network. Nevertheless, the cellular network may not be sufficient to handle peak loads and may fail to provide services when the city needs it most. A promising solution, object of this study, is to rely on alternative communication channels to alleviate the cellular infrastructure. This thesis investigates the transmission of data using vehicular networks as a data offloading channel. More specifically, it is analyzed a vehicular network of public transport vehicles, composed by buses and taxis, which circulate at the city throughout the day. Relying on the ubiquitous presence of buses and taxis to help gather and move data sounds promising, nevertheless we must make sure that they can collectively provide the necessary dynamics and capacity to satisfy the transmissions requirements. It is analyzed the dynamics and capacity of the vehicular network by considering different wireless technologies currently available. Given the scale of the network and the huge number of interactions between vehicles, we first reduce the complexity of the system by adopting a grid topology and grouping in clusters nearby nodes using the STING algorithm. Our analyses reveal aspects like vehicles having different patterns of movements. Also, buses have a lower mean time than taxis to reach the next transfer point. Finally, the results show that significant amounts of data can be offloaded onto the vehicular network in the city of Rio de Janeiro, reaching 500 TB daily