Abstract
Industrial IoT systems, such as those based on Autonomous Guided Vehicles (AGV), often generate a massive volume of data that needs to be processed and sent over to the cloud or private data centers. The presented research proposes and evaluates the approaches to data aggregation that help reduce the volume of readings from AGVs, by taking advantage of the edge computing paradigm. For the purposes of this article, we developed the processing workflow that retrieves data from AGVs, persists it in the local edge database, aggregates it in predefined time windows, and sends it to the cloud for further processing. We proposed two aggregation methods used in the considered workflow. We evaluated the developed workflow with different data sets and ran the experiments that allowed us to highlight the data volume reduction for each tested scenario. The results of the experiments show that solutions based on edge devices such as Jetson Xavier NX and technologies such as TimescaleDB can be successfully used to reduce the volume of data in pipelines that process data from Autonomous Guided Vehicles. Additionally, the use of edge computing paradigms improves the resilience to data loss in cases of network failures in such industrial systems.
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Acknowledgments
The research was supported by the Polish Ministry of Science and Higher Education as a part of the CyPhiS program at the Silesian University of Technology, Gliwice, Poland (Contract No. POWR.03.02.00-00-I007/17-00), the Norway Grants 2014-2021 operated by the National Centre for Research and Development under the project “Automated Guided Vehicles integrated with Collaborative Robots for Smart Industry Perspective” (Project Contract no.: NOR/POL-NOR/CoBotAGV/0027/2019-00) and by Statutory Research funds of Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland (grant No BK/RAu7/2022).
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Grzesik, P., Benecki, P., Kostrzewa, D., Shubyn, B., Mrozek, D. (2022). On-Edge Aggregation Strategies over Industrial Data Produced by Autonomous Guided Vehicles. In: Groen, D., de Mulatier, C., Paszynski, M., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2022. ICCS 2022. Lecture Notes in Computer Science, vol 13353. Springer, Cham. https://doi.org/10.1007/978-3-031-08760-8_39
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DOI: https://doi.org/10.1007/978-3-031-08760-8_39
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