Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT
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 oper...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/8190688 |
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| _version_ | 1832552721739153408 |
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| author | Rongqun Peng Xiuhua Fu Tian Ding |
| author_facet | Rongqun Peng Xiuhua Fu Tian Ding |
| author_sort | Rongqun Peng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8649b2335a3b415d8e947e40bd153f4a |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-8649b2335a3b415d8e947e40bd153f4a2025-02-03T05:57:56ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/8190688Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoTRongqun Peng0Xiuhua Fu1Tian Ding2School of Computer Science and TechnologySchool of Computer Science and TechnologySchool of Computer Science and TechnologyThe 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.http://dx.doi.org/10.1155/2022/8190688 |
| spellingShingle | Rongqun Peng Xiuhua Fu Tian Ding Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT Discrete Dynamics in Nature and Society |
| title | Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT |
| title_full | Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT |
| title_fullStr | Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT |
| title_full_unstemmed | Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT |
| title_short | Machine Learning with Variable Sampling Rate for Traffic Prediction in 6G MEC IoT |
| title_sort | machine learning with variable sampling rate for traffic prediction in 6g mec iot |
| url | http://dx.doi.org/10.1155/2022/8190688 |
| work_keys_str_mv | AT rongqunpeng machinelearningwithvariablesamplingratefortrafficpredictionin6gmeciot AT xiuhuafu machinelearningwithvariablesamplingratefortrafficpredictionin6gmeciot AT tianding machinelearningwithvariablesamplingratefortrafficpredictionin6gmeciot |