Data Prediction of Mobile Network Traffic in Public Scenes by SOS-<i>v</i>SVR Method
Accurate base station traffic data in a public place with large changes in the amount of people could help predict the occurrence of network congestion, which would allow us to effectively allocate network resources. This is of great significance for festival network support, routine maintenance, an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/3/603 |
| _version_ | 1828120027503525888 |
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| author | Xiaoliang Zheng Wenhao Lai Hualiang Chen Shen Fang |
| author_facet | Xiaoliang Zheng Wenhao Lai Hualiang Chen Shen Fang |
| author_sort | Xiaoliang Zheng |
| collection | DOAJ |
| description | Accurate base station traffic data in a public place with large changes in the amount of people could help predict the occurrence of network congestion, which would allow us to effectively allocate network resources. This is of great significance for festival network support, routine maintenance, and resource scheduling. However, there are a few related reports on base station traffic prediction, especially base station traffic prediction in public scenes with fluctuations in people flow. This study proposes a public scene traffic data prediction method, which is based on a <inline-formula> <math display="inline"> <semantics> <mrow> <mo> </mo> <mi>v</mi> </mrow> </semantics> </math> </inline-formula> Support Vector Regression (<i>v</i>SVR) algorithm. To achieve optimal prediction of traffic, a symbiotic organisms search (SOS) was adopted to optimize the <i>v</i>SVR parameters. Meanwhile, the optimal input time step was determined through a large number of experiments. Experimental data was obtained at the base station of Huainan Wanda Plaza, in the Anhui province of China, for three months, with the granularity being one hour. To verify the predictive performance of <i>v</i>SVR, the classic regression algorithm extreme learning machine (ELM) and variational Bayesian Linear Regression (vBLR) were used. Their optimal prediction results were compared with <i>v</i>SVR predictions. Experimental results show that the prediction results from SOS-<i>v</i>SVR were the best. Outcomes of this study could provide guidance for preventing network congestion and improving the user experience. |
| first_indexed | 2024-04-11T13:58:39Z |
| format | Article |
| id | doaj.art-de388d4b258d4aceafde5b4060fb9390 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T13:58:39Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-de388d4b258d4aceafde5b4060fb93902022-12-22T04:20:11ZengMDPI AGSensors1424-82202020-01-0120360310.3390/s20030603s20030603Data Prediction of Mobile Network Traffic in Public Scenes by SOS-<i>v</i>SVR MethodXiaoliang Zheng0Wenhao Lai1Hualiang Chen2Shen Fang3State Key Laboratory of Mining Response and Disaster Prevention and Control in Deep Coal Mines, Anhui University of Science and Technology, Huainan 232000, ChinaSchool of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232000, ChinaSchool of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232000, ChinaHuainan Branch of China Mobile Group Anhui Company Limited, Huainan 232000, ChinaAccurate base station traffic data in a public place with large changes in the amount of people could help predict the occurrence of network congestion, which would allow us to effectively allocate network resources. This is of great significance for festival network support, routine maintenance, and resource scheduling. However, there are a few related reports on base station traffic prediction, especially base station traffic prediction in public scenes with fluctuations in people flow. This study proposes a public scene traffic data prediction method, which is based on a <inline-formula> <math display="inline"> <semantics> <mrow> <mo> </mo> <mi>v</mi> </mrow> </semantics> </math> </inline-formula> Support Vector Regression (<i>v</i>SVR) algorithm. To achieve optimal prediction of traffic, a symbiotic organisms search (SOS) was adopted to optimize the <i>v</i>SVR parameters. Meanwhile, the optimal input time step was determined through a large number of experiments. Experimental data was obtained at the base station of Huainan Wanda Plaza, in the Anhui province of China, for three months, with the granularity being one hour. To verify the predictive performance of <i>v</i>SVR, the classic regression algorithm extreme learning machine (ELM) and variational Bayesian Linear Regression (vBLR) were used. Their optimal prediction results were compared with <i>v</i>SVR predictions. Experimental results show that the prediction results from SOS-<i>v</i>SVR were the best. Outcomes of this study could provide guidance for preventing network congestion and improving the user experience.https://www.mdpi.com/1424-8220/20/3/603public scenemobile network traffic data prediction<i>v</i>svrsymbiotic organisms search |
| spellingShingle | Xiaoliang Zheng Wenhao Lai Hualiang Chen Shen Fang Data Prediction of Mobile Network Traffic in Public Scenes by SOS-<i>v</i>SVR Method Sensors public scene mobile network traffic data prediction <i>v</i>svr symbiotic organisms search |
| title | Data Prediction of Mobile Network Traffic in Public Scenes by SOS-<i>v</i>SVR Method |
| title_full | Data Prediction of Mobile Network Traffic in Public Scenes by SOS-<i>v</i>SVR Method |
| title_fullStr | Data Prediction of Mobile Network Traffic in Public Scenes by SOS-<i>v</i>SVR Method |
| title_full_unstemmed | Data Prediction of Mobile Network Traffic in Public Scenes by SOS-<i>v</i>SVR Method |
| title_short | Data Prediction of Mobile Network Traffic in Public Scenes by SOS-<i>v</i>SVR Method |
| title_sort | data prediction of mobile network traffic in public scenes by sos i v i svr method |
| topic | public scene mobile network traffic data prediction <i>v</i>svr symbiotic organisms search |
| url | https://www.mdpi.com/1424-8220/20/3/603 |
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