Abstract
With the rapid development of the Internet of Things technology, the use of sensors to collect data has become a more popular method, but because of the different environment of the sensor, the working state of the sensor must be relatively high, such as in greenhouses such as high temperatures In a high-humidity environment, whether the sensor can work normally; in a continuous working condition of 7 * 24 h, whether the data collected by the sensor can be trusted is a question that must be considered. In the real environment, the environment is complex and changeable. The data collected by the sensor may be inaccurate. In more extreme cases, the sensor may not collect data, that is, there is missing data. In order to deal with the missing data, this article takes a greenhouse as an example, and compares the four dimensions of light intensity, carbon dioxide concentration, temperature, and humidity, and uses multiple interpolation methods to fill in missing sensor data.
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This work is supported in part by the Smart Agricultural Engineering Research Center of Jilin Province Foundation and the “Digital Agriculture” key discipline of JiLin province Foundation.
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Li, Z., Liu, Y., Yu, H., Tang, Y. (2021). Assumption of Missing Processing of Sensor Acquisition Data Based on Multiple Interpolation. In: Atiquzzaman, M., Yen, N., Xu, Z. (eds) Big Data Analytics for Cyber-Physical System in Smart City. BDCPS 2020. Advances in Intelligent Systems and Computing, vol 1303. Springer, Singapore. https://doi.org/10.1007/978-981-33-4572-0_123
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DOI: https://doi.org/10.1007/978-981-33-4572-0_123
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