Delay Prediction in M2M Networks Using the Deep Learning Approach

The rapid development of fifth-generation (5G) mobile technology and its applications in various sectors has increased the number of machine-to-machine (M2M) connections and network traffic generated by M2M devices. On the other hand, inadequate management of network services and potential cybersecurity risks could result from the insufficient analysis of the growing M2M traffic. In this chapter, we apply deep learning based on a long short-term memory (LSTM) model to implement time-series prediction of M2M traffic. The mean absolute percentage error (MAPE) and the root mean square error (RMSE) were used to evaluate the prediction accuracy.