CSVM: uma plataforma para crowdSensing móvel dirigida por modelos em tempo de execução
Carregando...
Data
2014-10-15
Autores
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Federal de Goiás
Resumo
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.
Descrição
Palavras-chave
Citação
MELO, P. C. F. CSVM: uma plataforma para crowdSensing móvel dirigida por modelos em tempo de execução. 2014. 146 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2014.