Towards Edge-Based Data Lake Architecture for Intelligent Transportation System

TL;DR

An Edge-based Data Lake Architecture is proposed to integrate and analyze the complex data from ITS efficiently, and offers scalability, fault tolerance, and performance, improving decision-making and enhancing innovative services for a more intelligent transportation ecosystem.

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.

Figures (7)

• Figure 1: Edge-based data lake architecture
• Figure 2: MEC Implementation
• Figure 3: Evaluated use case scenarios.
• Figure 4: Aggregation rate obtained using the centrality-based algorithm compared to the RB algorithm.
• Figure 5: Simulated UE's route at a region in São Paulo city, Brazil, considering real-world eNB locations.