A data acquisition model for industrial gateways based on edge computing and MapReduce

Siying Li; Ling Zhu; Hong Chen; Zijin Liu; Guangyu Liu; Hanyan Dong

doi:10.1117/12.3010988

1 December 2023 A data acquisition model for industrial gateways based on edge computing and MapReduce

Siying Li, Ling Zhu, Hong Chen, Zijin Liu, Guangyu Liu, Hanyan Dong

Proceedings Volume 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023); 1294031 (2023) https://doi.org/10.1117/12.3010988
Event: Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 2023, Sipsongpanna, China

Abstract

In the current power system, the existing industrial gateways suffer from drawbacks including sluggish sampling rates, inadequate precision in data acquisition, and fragmented data collection points. To mitigate the excessive burden on production lines resulting from increased interaction caused by the aforementioned issues, this paper presents an advanced data acquisition model for industrial gateways. Based on edge computing and MapReduce, this model significantly improves the efficiency and performance of data collection from industrial production line points. By enhancing the gateway's data acquisition component and ensuring the security of gateway data, our proposed approach outperforms existing industrial gateways.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Siying Li, Ling Zhu, Hong Chen, Zijin Liu, Guangyu Liu, and Hanyan Dong "A data acquisition model for industrial gateways based on edge computing and MapReduce", Proc. SPIE 12940, Third International Conference on Control and Intelligent Robotics (ICCIR 2023), 1294031 (1 December 2023); https://doi.org/10.1117/12.3010988

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