Towards Reference Architectures: a Cloud-agnostic Data Analytics Platform Empowering Autonomous Systems

This work introduces a scalable, cloud-agnostic and fault-tolerant data analytics platform for state-of-the-art autonomous systems that is built from open-source, reusable building blocks. As the baseline for further new reference architectures, it represents an architecture blueprint for processing...

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Bibliographic Details
Published inIEEE access p. 1
Main Authors Marosi, Attila Csaba, Emodi, Mark, Farkas, Attila, Lovas, Robert, Beregi, Richard, Pedone, Gianfranco, Nemeth, Balazs, Gaspar, Peter
Format Journal Article
LanguageEnglish
Published IEEE 03.06.2022
Subjects
Architecture
Autonomous systems
Big Data
Blueprint
Cloud computing
collaborative robots
Computer architecture
Data analysis
Data Analytics
IIoT
IoT
Measurement
mobile robots
Open source software
Reference Architecture
smart control
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Summary:This work introduces a scalable, cloud-agnostic and fault-tolerant data analytics platform for state-of-the-art autonomous systems that is built from open-source, reusable building blocks. As the baseline for further new reference architectures, it represents an architecture blueprint for processing, enriching and analyzing various feeds of structured and non-structured input data from advanced Internet-of-Things (IoT) based use cases. The platform builds on industry best practices, leverages on solid open-source components in a reusable fashion, and is based on our experience gathered from numerous IoT and Big Data research projects. The platform is currently used in the framework of the National Laboratory for Autonomous Systems in Hungary (abbreviated as ARNL). The platform is demonstrated through selected use cases from ARNL including the areas of smart/autonomous production systems (collaborative robotic assembly) and autonomous vehicles (mobile robots with smart vehicle control). Finally, we validate the platform through the evaluation of its streaming ingestion capabilities.
ISSN:2169-3536
DOI:10.1109/ACCESS.2022.3180365