Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT
Open Access
- 17 November 2022
- journal article
- research article
- Published by Hindawi Limited in Discrete Dynamics in Nature and Society
- Vol. 2022, 1-11
- https://doi.org/10.1155/2022/8190688
Abstract
The high-speed development of mobile broadband networks and IoT applications has brought about massive data transmission and data processing, and severe traffic congestion has adversely affected the fast-growing networks and industries. To better allocate network resources and ensure the smooth operation of communications, predicting network traffic becomes an important tool. We investigate in detail the impact of variable sampling rate on traffic prediction and propose a high-speed traffic prediction method using machine learning and recurrent neural networks. We first investigate a VSR-NLMS adaptive prediction method to perform time series prediction dataset transformation. Then, we propose a VSR-LSTM algorithm for real-time prediction of network traffic. Finally, compared with the traditional traffic prediction algorithm based on fixed sampling rate (FSR-LSTM), we simulate the prediction accuracy of the VSR-LSTM algorithm based on the variable sampling rate proposed. The experiment shows that VSR-LSTM has higher traffic prediction accuracy because its sampling rate varies with the traffic.Funding Information
- State Key Laboratory of Networking and Switching Technology (SKLNST, ‐, 2020, 1, 10)
This publication has 24 references indexed in Scilit:
- Resource Allocation With Edge Computing in IoT Networks via Machine LearningIEEE Internet of Things Journal, 2020
- Network Prediction with Traffic Gradient Classification using Convolutional Neural NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2020
- Research on Network Traffic Prediction Model Based on Neural NetworkPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2019
- Dynamic Traffic Prediction with Adaptive Sampling for 5G HetNet IoT ApplicationsWireless Communications and Mobile Computing, 2019
- Deep Learning with Long Short-Term Memory for Time Series PredictionIEEE Communications Magazine, 2019
- Research on Network Traffic Prediction Based on Long Short-Term Memory Neural NetworkPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- A handover statistics based approach for Cell Outage Detection in self-organized Heterogeneous NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Traffic Prediction for Reliable and Resilient Video Communications Over Multi-Location WMNsJournal of Network and Systems Management, 2016
- Long Short-Term MemoryNeural Computation, 1997
- Neural networks and physical systems with emergent collective computational abilities.Proceedings of the National Academy of Sciences of the United States of America, 1982