A streaming data prediction method based on long short-term memory model and grey model

Liping Yang; Sisi Sun; Rui Xin

doi:10.1117/12.2615305

15 October 2021 A streaming data prediction method based on long short-term memory model and grey model

Liping Yang, Sisi Sun, Rui Xin

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119332D (2021) https://doi.org/10.1117/12.2615305
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

The popularity of intelligent devices has driven the application and development of Internet of Things (IoT). Data generated by sensor networks are more inclined to be transmitted and processed in the form of streaming data. The appearance of edge computing can effectively relieve the pressure of network bandwidth. However, at the edge of the network, the traffic is often uncertain, which leads to the fluctuation of network bandwidth and load. Therefore, it is of great significance to predict the future traffic according to the previous data transmission volume and current data volume of each node of the network. In order to improve the efficiency of edge unloading and the resource utilization of edge computing server, a method based on long short-term memory (LSTM) model and grey model is proposed to predict the traffic over a period of time, and the device and equipment corresponding to the method are given. The simulation results show that the method can effectively solve the data storage and calculation of IoT terminals, and improve the accuracy of IoT edge traffic prediction.

Citation Download Citation

Liping Yang, Sisi Sun, and Rui Xin "A streaming data prediction method based on long short-term memory model and grey model", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119332D (15 October 2021); https://doi.org/10.1117/12.2615305

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