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

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Computational Science – ICCS 2022 (ICCS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13353))

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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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References

  1. Jetson Xavier NX specification. https://developer.nvidia.com/embedded/jetson-xavier-nx-devkit. Accessed 5 Feb 2022

  2. PostgreSQL documentation. https://www.postgresql.org/about/. Accessed 9 Feb 2022

  3. TimescaleDB documentation. https://docs.timescale.com/latest/introduction. Accessed 9 Feb 2022

  4. TimescaleDB: SQL made scalable for time-series data (2017). https://pdfs.semanticscholar.org/049a/af11fa98525b663da18f39d5dcc5d345eb9a.pdf

  5. Bouslama, A., Laaziz, Y., Tali, A., Mohamed, E.: AWS and IoT for real-time remote medical monitoring. Int. J. Intell. Enterp. 6, 293–310 (2019)

    Google Scholar 

  6. Cupek, R., et al.: Autonomous guided vehicles for smart industries – the state-of-the-art and research challenges. In: Krzhizhanovskaya, V.V., et al. (eds.) ICCS 2020. LNCS, vol. 12141, pp. 330–343. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-50426-7_25

    Chapter  Google Scholar 

  7. Fadhel, M., Sekerinski, E., Yao, S.: A comparison of time series databases for storing water quality data. In: Auer, M.E., Tsiatsos, T. (eds.) IMCL 2018. AISC, vol. 909, pp. 302–313. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-11434-3_33

    Chapter  Google Scholar 

  8. Gaur, A., Scotney, B., Parr, G., McClean, S.: Smart city architecture and its applications based on IoT. Procedia Comput. Sci. 52, 1089–1094 (2015)

    Article  Google Scholar 

  9. Greco, L., Ritrovato, P., Xhafa, F.: An edge-stream computing infrastructure for real-time analysis of wearable sensors data. Future Gener. Comput. Syst. 93, 515–528 (2019). https://www.sciencedirect.com/science/article/pii/S0167739X18314031

  10. Grzesik, P., Mrozek, D.: Metagenomic analysis at the edge with Jetson Xavier NX. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds.) ICCS 2021. LNCS, vol. 12745, pp. 500–511. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-77970-2_38

    Chapter  Google Scholar 

  11. Hu, L., Miao, Y., Wu, G., Hassan, M.M., Humar, I.: iRobot-Factory: an intelligent robot factory based on cognitive manufacturing and edge computing. Future Gener. Comput. Syst. 90, 569–577 (2019). https://www.sciencedirect.com/science/article/pii/S0167739X1831183X

  12. Jaiganesh, S., Gunaseelan, K., Ellappan, V.: IOT agriculture to improve food and farming technology. In: 2017 Conference on Emerging Devices and Smart Systems (ICEDSS), pp. 260–266 (2017)

    Google Scholar 

  13. Liu, X., Nielsen, P.S.: Air quality monitoring system and benchmarking. In: Bellatreche, L., Chakravarthy, S. (eds.) DaWaK 2017. LNCS, vol. 10440, pp. 459–470. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-64283-3_34

    Chapter  Google Scholar 

  14. Munir, M.S., Bajwa, I.S., Ashraf, A., Anwar, W., Rashid, R.: Intelligent and smart irrigation system using edge computing and IoT. Complexity 2021, 6691571 (2021). https://doi.org/10.1155/2021/6691571

    Article  Google Scholar 

  15. Nandyala, C.S., Kim, H.K.: Green IoT agriculture and healthcare application (GAHA). Int. J. Smart Home 10(4), 289–300 (2016)

    Article  Google Scholar 

  16. Neelakandan, S., Berlin, M., Tripathi, S., Devi, V.B., Bhardwaj, I., Arulkumar, N.: IoT-based traffic prediction and traffic signal control system for smart city. Soft. Comput. 25(18), 12241–12248 (2021)

    Article  Google Scholar 

  17. Paul, A., Pinjari, H., Hong, W.H., Seo, H., Rho, S.: Fog computing-based IoT for health monitoring system. J. Sens. 2018, 1–7 (2018)

    Article  Google Scholar 

  18. Raileanu, S., Borangiu, T., Morariu, O., Iacob, I.: Edge computing in industrial IoT framework for cloud-based manufacturing control. In: 2018 22nd International Conference on System Theory, Control and Computing (ICSTCC), pp. 261–266 (2018)

    Google Scholar 

  19. Rajavel, R., Ravichandran, S.K., Harimoorthy, K., Nagappan, P., Gobichettipalayam, K.R.: IoT-based smart healthcare video surveillance system using edge computing. J. Ambient Intell. Humaniz. Comput. (2021). https://doi.org/10.1007/s12652-021-03157-1

  20. Renart, E.G., Diaz-Montes, J., Parashar, M.: Data-driven stream processing at the edge. In: 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC), pp. 31–40 (2017)

    Google Scholar 

  21. Sabireen, H., Neelanarayanan, V.: A review on fog computing: architecture, fog with IoT, algorithms and research challenges. ICT Express 7(2), 162–176 (2021)

    Article  Google Scholar 

  22. Singh, S.: Optimize cloud computations using edge computing. In: 2017 International Conference on Big Data, IoT and Data Science (BID), pp. 49–53, December 2017

    Google Scholar 

  23. Wang, X., Garg, S., Lin, H., Kaddoum, G., Hu, J., Alhamid, M.F.: An intelligent UAV based data aggregation algorithm for 5G-enabled internet of things. Comput. Netw. 185, 107628 (2021). https://www.sciencedirect.com/science/article/pii/S138912862031255X

  24. Wu, Z., Zhou, C.: Equestrian sports posture information detection and information service resource aggregation system based on mobile edge computing. Mob. Inf. Syst. 2021, 4741912, July 2021. https://doi.org/10.1155/2021/4741912

  25. Xhafa, F., Kilic, B., Krause, P.: Evaluation of IoT stream processing at edge computing layer for semantic data enrichment. Future Gener. Comput. Syst. 105, 730–736 (2020). https://www.sciencedirect.com/science/article/pii/S0167739X19321296

  26. Yar, H., Imran, A.S., Khan, Z.A., Sajjad, M., Kastrati, Z.: Towards smart home automation using IoT-enabled edge-computing paradigm. Sensors 21(14) (2021). https://www.mdpi.com/1424-8220/21/14/4932

  27. Zhang, H., Zhang, Z., Zhang, L., Yang, Y., Kang, Q., Sun, D.: Object tracking for a smart city using IoT and edge computing. Sensors 19(9) (2019). https://www.mdpi.com/1424-8220/19/9/1987

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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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Authors and Affiliations

  1. Department of Applied Informatics, Silesian University of Technology, Gliwice, Poland

    Piotr Grzesik, Paweł Benecki, Daniel Kostrzewa, Bohdan Shubyn & Dariusz Mrozek

  2. Department of Telecommunications, Lviv Polytechnic National University, Lviv, Ukraine

    Bohdan Shubyn

Authors
  1. Piotr Grzesik
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  2. Paweł Benecki
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  3. Daniel Kostrzewa
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  4. Bohdan Shubyn
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  5. Dariusz Mrozek
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Corresponding author

Correspondence to Piotr Grzesik .

Editor information

Editors and Affiliations

  1. Brunel University London, London, UK

    Derek Groen

  2. University of Amsterdam, Amsterdam, The Netherlands

    Clélia de Mulatier

  3. AGH University of Science and Technology, Krakow, Poland

    Maciej Paszynski

  4. University of Amsterdam, Amsterdam, The Netherlands

    Valeria V. Krzhizhanovskaya

  5. University of Tennessee at Knoxville, Knoxville, TN, USA

    Jack J. Dongarra

  6. University of Amsterdam, Amsterdam, The Netherlands

    Peter M. A. Sloot

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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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