State Prediction Simulation of Internet of Things Terminal Security Access Data Gateway Based on Deep Learning

With the rapid development of artificial intelligence, its applications in related fields are becoming more and more extensive and in-depth. Among them, deep neural network is the key technology to promote the rapid development of artificial intelligence. The access of IoT (Internet of things) terminals affects the channel capacity of the data gateway, resulting in unstable operation of the gateway. This paper proposes a data gateway state prediction method under the secure access of IoT terminals. The shortest path optimization method is used to deploy the gateway routing nodes, and the Internet of Things channel transmission model is constructed. Based on the single-cycle prediction model and the aggregated prediction model, a multi-cycle prediction model is constructed to realize the prediction of the security access data gateway status of IoT terminals. The simulation results show that the proposed method has higher prediction efficiency and better accuracy.