Danilo Fernandes, Douglas L. L. Moura, Gean Santos, Geymerson S. Ramos, Fabiane Queiroz, Andre L. L. Aquino
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引用次数: 0
Abstract
The rapid urbanization growth has underscored the need for innovative
solutions to enhance transportation efficiency and safety. Intelligent
Transportation Systems (ITS) have emerged as a promising solution in this
context. However, analyzing and processing the massive and intricate data
generated by ITS presents significant challenges for traditional data
processing systems. This work proposes an Edge-based Data Lake Architecture to
integrate and analyze the complex data from ITS efficiently. The architecture
offers scalability, fault tolerance, and performance, improving decision-making
and enhancing innovative services for a more intelligent transportation
ecosystem. We demonstrate the effectiveness of the architecture through an
analysis of three different use cases: (i) Vehicular Sensor Network, (ii)
Mobile Network, and (iii) Driver Identification applications.
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