Abstract (EN):
Thanks to the Internet of Things (IoT) revolution and advancements in embedded systems, the incorporation of an extensive array of sensors and computational resources into vehicles has become a reality, significantly enhancing their capabilities to produce relevant data. This has opened up new opportunities for acquiring and processing vehicular data in various areas, supporting new applications such as vehicular monitoring to the use of information for artificial intelligence for improved decision-making tasks. However, although the benefits are promising, there is a current challenge when providing flexible and standardized procedures to capture, process, and store this information for further analysis. In this context, this article proposes an framework to support the the handling of vehicular data, including data processing by a server and storage in a cloud database. This framework was validated through a real-world case study, analyzing the collected sensor data. The results indicated the feasibility of the proposal, contributing to the availability of vehicular data analysis in a way that is distinct from proposals in the literature. Additionally, this framework offers another significant contribution, related to the use of an appropriate database for time series, enabling scalability and high availability.
Language:
English
Type (Professor's evaluation):
Scientific
No. of pages:
6