Research on Prediction and Management of <scp>IoT</scp> Resource Supply Based on Machine Learning

ABSTRACT Network transmission plays an important role in information system services of IoT, and network transmission quality is also an important indicator of the quality of information export in China. To measure the development of Internet quality in China's foreign trade, this paper calculates and analyzes the time series data of Internet export quality based on open source. This paper first analyzes the necessity and significance of the research. After comparing several types of time series methods, this paper chooses a statistics‐based method to describe the Internet quality data, compares the statistical data before and after the epidemic, and draws a conclusion that the epidemic is irrelevant to the Internet quality. Further, this paper combined the cyclic neural network LSTM and principal component analysis methods to carry out calculations. LSTM method completed the analysis and prediction of the quality data of the Internet export, selected packet loss rate, RTT, and delay jitter as the indicators of Internet quality, and completed the preprocessing of the indicator data. To obtain comprehensive network quality indicators, this paper constructs a single index network quality evaluation model based on principal component analysis and completes the analysis and calculation of data analysis. The computation results demonstrate the great prediction accuracy of the LSTM referenced in this research for index data, and the only index result for measuring the quality of Internet exports can be obtained by using principal component analysis. In practical applications, the research in this paper can provide support for the subsequent network performance optimization and bandwidth utilization improvement of the IOT industry. Github link and data source of the paper is https://github.com/superpeace90/iotproject .