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
摘要
城市化的快速发展凸显了对创新解决方案的需求，以提高运输效率和安全性。在此背景下，智能交通系统（ITS）成为一种前景广阔的解决方案。然而，分析和处理智能交通系统产生的大量复杂数据对传统数据处理系统提出了巨大挑战。这项工作提出了一种基于边缘的数据湖架构，以有效整合和分析 ITS 的复杂数据。该架构提供了可扩展性、容错性和性能，可改善决策并增强创新服务，从而打造更加智能的交通生态系统。我们通过分析以下三种不同的使用案例来证明该架构的有效性：(i) 车辆传感器网络；(ii) 移动网络；(iii) 驾驶员识别应用。
Towards Edge-Based Data Lake Architecture for Intelligent Transportation System
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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