Abstract
This paper proposes Internet of Things (IoT) analytics and machine learning based on open-source software for environmental data collected from the monitored sites such as industrial and high-tech zones. The IoT platform needs to meet the functional requirements such as data collection and storage data for a long time, device management, real-time monitored parameters, security, and data analysis. We present the design method of an IoT platform with functions of data collection and storage, real-time data streaming, and how to apply Recurrent Neural Network (RNN) to predictive analysis of environmental parameters. We also implement data pipeline to serve machine learning model training as well as prediction. Initial prediction results for the ambient temperature parameter based on machine learning models and built-in IoT platforms can continue to be developed for many other environmental parameters.
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Acknowledgement
This research is funded by the Ministry of Science and Technology of Vietnam under the framework of “research and development of information technology products for e-Government” (Ref: KC.01/16-20) with the research project titled “Research and development of Internet of Things platform (IoT), application to management of high technology, industrial zones”, the mission code is KC.01.17/16-20.
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Trung, H.D., Dung, N.X., Trung, N.H. (2021). Building IoT Analytics and Machine Learning with Open Source Software for Prediction of Environmental Data. In: Abraham, A., Hanne, T., Castillo, O., Gandhi, N., Nogueira Rios, T., Hong, TP. (eds) Hybrid Intelligent Systems. HIS 2020. Advances in Intelligent Systems and Computing, vol 1375. Springer, Cham. https://doi.org/10.1007/978-3-030-73050-5_14
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DOI: https://doi.org/10.1007/978-3-030-73050-5_14
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