Uma nova versão do protocolo grant para redes tolerantes a atrasos

• Ano de defesa: 2018
• Autor(a) principal: Mendes, Luís Guilherme Bergamini
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Brasil; Programa de Pós-Graduação em Computação Aplicada; UTFPR
• Palavras-chave em Português: Rede de computadore - Protocolos; Roteadores (Redes de computadores); Tolerância a falha (Computadores); Inteligência coletiva; Otimização matemática; Algoritmos de formigas; Redes ad hoc (Redes de computadores); Computação; Computer network protocols; Routers (Computer network); Fault-tolerant computing; Swarm intelligence; Mathematical optimization; Ant algorithms; Ad hoc networks (Computer networks); Computer science; CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::SISTEMAS DE COMPUTACAO; Ciência da Computação
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/3468

This work presents a new version of the Greedy Ant (GrAnt) protocol, which uses the Ant Colony Optimization (ACO) meta-heuristic to route traffic in complex and dynamic Delay/Disruption Tolerant Networks. The new version, called rGrAnt, was developed to provide the protocol the ability to extract information online from nodes’ social connectivity, which can reside from disconnected and sparse to highly connected networking environments. With that information, rGrAnt can guide the ACO routing module using a set of rules for deciding (1) when to consider data from heuristic functions and/or pheromone concentration, (2) which data can be incorporated in both heuristic and pheromone parameters, and (3) if the message forwarding phase must be less or more restrictive. In nodes with low connectivity, GrAnt should be less restrictive when forwarding messages, in order to make better use of the few available contacts. In contrast, in nodes with high connectivity, it is necessary to restrict forwarding so as not to overload the same sets of nodes and links. rGrAnt was compared to GrAnt in three different mobility scenarios. Results show that, in the three scenarios, rGrAnt achieves a higher delivery ratio than GrAnt.