期刊
IEEE ACCESS
卷 10, 期 -, 页码 60658-60673出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2022.3180365
关键词
Computer architecture; Data analysis; Cloud computing; Architecture; Open source software; Autonomous systems; Measurement; Reference architecture; blueprint; data analytics; autonomous systems; IoT; IIoT; big data; mobile robots; collaborative robots; smart control
资金
- National Research, Development and Innovation (NRDI) Office-NKFIH, Hungary [ED_18-2-2018-0006, SNN 129178]
- Janos Bolyai Research Scholarship of the Hungarian Academy of Sciences
- Ministry of Innovation and Technology NRDI Office
This work introduces a scalable, cloud-agnostic, and fault-tolerant data analytics platform for state-of-the-art autonomous systems. It is built using open-source reusable building blocks and can process various feeds of structured and non-structured input data from advanced IoT use cases. The platform is currently used in the National Laboratory for Autonomous Systems in Hungary and has been validated through selected use cases.
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.
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