Abstract
Edge Computing is the new paradigm to process data at the edge of the network. The scenario of edge computing varies, depending on the case and problem. In this paper, we investigate an architecture that is suitable for Intelligence Aquaculture. This system will handle tasks such as collecting the water sensor data and running an Artificial Intelligence algorithm to train model prediction and run real-time object detection with a Deep learning algorithm and Deepstream. All applications were deployed with a docker container and managed with Lightweight Kubernetes (K3s). Rancher is also used to coordinate and visualize the resource system of the edge devices. This system architecture could be a reference for edge computing ecosystems and monitoring system of Aquaculture.
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Acknowledgements
This work was supported by the Ministry of Science and Technology, Taiwan (R.O.C.), under grants number 110-2221-E-029-020-MY3 and 110-2811-E-029 -003.
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Fathoni, H., Yang, CT., Huang, CY., Chen, CY., Hsieh, TF. (2022). Aquaculture Monitoring Systems Based on Lightweight Kubernetes and Rancher. In: Lin, YB., Deng, DJ., Yang, CT. (eds) Smart Grid and Internet of Things. SGIoT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 447. Springer, Cham. https://doi.org/10.1007/978-3-031-20398-5_4
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