CSVM: uma plataforma para crowdSensing móvel dirigida por modelos em tempo de execução

• Ano de defesa: 2014
• Autor(a) principal: Melo, Paulo César Ferreira
• Orientador(a): Costa, Fábio Moreira
• Banca de defesa: Costa, Fábio Moreira, Carvalho, Sérgio Teixeira de, Ferraz, Carlos André Guimarães
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Goiás
• Programa de Pós-Graduação: Programa de Pós-graduação em Ciência da Computação (INF)
• Departamento: Instituto de Informática - INF (RG)
• País: Brasil
• Palavras-chave em Português: Crowdsensing móvel; Modelos em tempo de execução; Internet das coisas
• Palavras-chave em Inglês: Mobile crowdsensing; Models at runtime; Internet of things
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://repositorio.bc.ufg.br/tede/handle/tede/4766

Recent advances in ubiquitous computing have contributed to the rise of an emerging category of mobile devices that have computational and sensing capabilities, such as smartphones and wearable devices. The widespread use of these devices connected by communication networks contribute to the evolution of the Internet of Things. The presence of these mobile devices increases the chance for the development of applications using the sensing ability of these devices to measure, and understand the environmental indicators. Furthemore, data sensed by these applications can be shared among different mobile devices, giving rise to a paradigm called mobile crowdsensing. The complexity of applications in this domain is associated with factors such as interoperability between different mobile devices, data identification and capture from these devices, and adaptation of their use in heterogeneous and dynamic environments. Software engineering approaches such as Model-Driven Engineering (MDE) and, more specifically, models at runtime are an effective way of dealing with this complexity. We propose the use of an approach based on models at runtime for creating and processing mobile crowdsensing queries.We show how this approach can be used by defining a domain-specific modeling language for the mobile crowdsensing domain, called CSML. We built and validated the CSML metamodel which captures the main aspects of the domain, and its execution environment, which consists of an execution engine for models described in CSML, called CSVM. This approach facilitates the specification of mobile crowdsensing queries, also enabling their dynamic change during their processing.