KEYWORDS: Data modeling, Data transmission, Data acquisition, Internet, Instrument modeling, Sensors, Data processing, Software, Performance modeling, Neural networks
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.
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