Toward Reference Architectures: A Cloud-Agnostic Data Analytics Platform Empowering Autonomous Systems

IEEE Access

Attila Csaba Marosi¹  Gianfranco Pedone¹  Richard Beregi²  Robert Lovas³  Attila Farkas⁴  Peter Gaspar⁴  Mark Emodi⁵  Balazs Nemeth⁵ 

[1] Laboratory of Parallel and Distributed Systems, Institute for Computer Science and Control, E&x00F6;nd Research Network, Budapest, Hungary;r&x00E1;s L&x00F3;tv&x00F6;

关键词: Reference architecture;    blueprint;    data analytics;    autonomous systems;    IoT;    IIoT;   

DOI  :  10.1109/ACCESS.2022.3180365

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown