On-Edge Aggregation Strategies over Industrial Data Produced by Autonomous Guided Vehicles

Piotr Grzesik; Paweł Benecki; Daniel Kostrzewa; Bohdan Shubyn; Dariusz Mrozek

doi:10.1007/978-3-031-08760-8_39

On-Edge Aggregation Strategies over Industrial Data Produced by Autonomous Guided Vehicles

Lviv Polytechnic National University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

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.

Original language	English
Title of host publication	Computational Science - ICCS 2022, 22nd International Conference, Proceedings
Editors	Derek Groen, Clélia de Mulatier, Valeria V. Krzhizhanovskaya, Peter M.A. Sloot, Maciej Paszynski, Jack J. Dongarra
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	458-471
Number of pages	14
ISBN (Print)	9783031087592
DOIs	https://doi.org/10.1007/978-3-031-08760-8_39
Publication status	Published - 2022
Event	22nd Annual International Conference on Computational Science, ICCS 2022 - London, United Kingdom Duration: 21 Jun 2022 → 23 Jun 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13353 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd Annual International Conference on Computational Science, ICCS 2022
Country/Territory	United Kingdom
City	London
Period	21/06/22 → 23/06/22

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Automated guided vehicles
Cloud computing
Data aggregations
Edge analytics
Edge computing
Internet of things
TimescaleDB

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-08760-8_39

Cite this

Grzesik, P., Benecki, P., Kostrzewa, D., Shubyn, B., & Mrozek, D. (2022). On-Edge Aggregation Strategies over Industrial Data Produced by Autonomous Guided Vehicles. In D. Groen, C. de Mulatier, V. V. Krzhizhanovskaya, P. M. A. Sloot, M. Paszynski, & J. J. Dongarra (Eds.), Computational Science - ICCS 2022, 22nd International Conference, Proceedings (pp. 458-471). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Vol. 13353 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-08760-8_39

Grzesik, Piotr ; Benecki, Paweł ; Kostrzewa, Daniel et al. / On-Edge Aggregation Strategies over Industrial Data Produced by Autonomous Guided Vehicles. Computational Science - ICCS 2022, 22nd International Conference, Proceedings. editor / Derek Groen ; Clélia de Mulatier ; Valeria V. Krzhizhanovskaya ; Peter M.A. Sloot ; Maciej Paszynski ; Jack J. Dongarra. Springer Science and Business Media Deutschland GmbH, 2022. pp. 458-471 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ).

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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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Grzesik, P , Benecki, P , Kostrzewa, D , Shubyn, B & Mrozek, D 2022, On-Edge Aggregation Strategies over Industrial Data Produced by Autonomous Guided Vehicles. in D Groen, C de Mulatier, VV Krzhizhanovskaya, PMA Sloot, M Paszynski & JJ Dongarra (eds), Computational Science - ICCS 2022, 22nd International Conference, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , vol. 13353 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 458-471, 22nd Annual International Conference on Computational Science, ICCS 2022, London, United Kingdom, 21/06/22. https://doi.org/10.1007/978-3-031-08760-8_39

On-Edge Aggregation Strategies over Industrial Data Produced by Autonomous Guided Vehicles. / Grzesik, Piotr ; Benecki, Paweł ; Kostrzewa, Daniel et al.
Computational Science - ICCS 2022, 22nd International Conference, Proceedings. ed. / Derek Groen; Clélia de Mulatier; Valeria V. Krzhizhanovskaya; Peter M.A. Sloot; Maciej Paszynski; Jack J. Dongarra. Springer Science and Business Media Deutschland GmbH, 2022. p. 458-471 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ; Vol. 13353 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - On-Edge Aggregation Strategies over Industrial Data Produced by Autonomous Guided Vehicles

AU - Grzesik, Piotr

AU - Benecki, Paweł

AU - Kostrzewa, Daniel

AU - Shubyn, Bohdan

AU - Mrozek, Dariusz

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Y1 - 2022

N2 - 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.

AB - 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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KW - Cloud computing

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KW - Internet of things

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DO - 10.1007/978-3-031-08760-8_39

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Grzesik P , Benecki P , Kostrzewa D , Shubyn B , Mrozek D. On-Edge Aggregation Strategies over Industrial Data Produced by Autonomous Guided Vehicles. In Groen D, de Mulatier C, Krzhizhanovskaya VV, Sloot PMA, Paszynski M, Dongarra JJ, editors, Computational Science - ICCS 2022, 22nd International Conference, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 458-471. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) ). doi: 10.1007/978-3-031-08760-8_39