A Generative Model for Traffic Demand with Heterogeneous and Spatiotemporal Characteristics in Massive Wi-Fi Systems
A substantial amount of money and time is required to optimize resources in a massive Wi-Fi network in a real-world environment. Therefore, to reduce cost, proposed algorithms are first verified through simulations before implementing them in a real-world environment. A traffic model is essential to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-06-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/11/12/1848 |
| _version_ | 1797488006903365632 |
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| author | Jae-Min Lee Jong-Deok Kim |
| author_facet | Jae-Min Lee Jong-Deok Kim |
| author_sort | Jae-Min Lee |
| collection | DOAJ |
| description | A substantial amount of money and time is required to optimize resources in a massive Wi-Fi network in a real-world environment. Therefore, to reduce cost, proposed algorithms are first verified through simulations before implementing them in a real-world environment. A traffic model is essential to describe user traffic for simulations. Existing traffic models are statistical models based on a discrete-time random process and combine a spatiotemporal characteristic model with the varying parameters, such as average and variance, of a statistical model. The spatiotemporal characteristic model has a mathematically strict assumption that the access points (APs) have approximately similar traffic patterns that increase during day times and decrease at night. The mathematical assumption ensures a homogeneous representation of the network traffic. It does not include heterogeneous characteristics, such as the fact that lecture buildings on campus have a high traffic during lectures, while restaurants have a high traffic only during mealtimes. Therefore, it is difficult to represent heterogeneous traffic using this mathematical model. Deep learning can be used to represent heterogeneous patterns. This study proposes a generative model for Wi-Fi traffic that considers spatiotemporal characteristics using deep learning. The proposed model learns the heterogeneous traffic patterns from the AP-level measurement data without any assumptions and generates similar traffic patterns based on the data. The result shows that the difference between the sample generated by the proposed model and the collected data is up to 72.1% less than that reported in previous studies. |
| first_indexed | 2024-03-09T23:56:55Z |
| format | Article |
| id | doaj.art-dee141e3c95441339219c2bb69456c12 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-09T23:56:55Z |
| publishDate | 2022-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-dee141e3c95441339219c2bb69456c122023-11-23T16:24:40ZengMDPI AGElectronics2079-92922022-06-011112184810.3390/electronics11121848A Generative Model for Traffic Demand with Heterogeneous and Spatiotemporal Characteristics in Massive Wi-Fi SystemsJae-Min Lee0Jong-Deok Kim1Department of Information Convergence Engineering, Pusan National University, Busan 46241, KoreaSchool of Computer Science and Engineering, Pusan National University, Busan 46241, KoreaA substantial amount of money and time is required to optimize resources in a massive Wi-Fi network in a real-world environment. Therefore, to reduce cost, proposed algorithms are first verified through simulations before implementing them in a real-world environment. A traffic model is essential to describe user traffic for simulations. Existing traffic models are statistical models based on a discrete-time random process and combine a spatiotemporal characteristic model with the varying parameters, such as average and variance, of a statistical model. The spatiotemporal characteristic model has a mathematically strict assumption that the access points (APs) have approximately similar traffic patterns that increase during day times and decrease at night. The mathematical assumption ensures a homogeneous representation of the network traffic. It does not include heterogeneous characteristics, such as the fact that lecture buildings on campus have a high traffic during lectures, while restaurants have a high traffic only during mealtimes. Therefore, it is difficult to represent heterogeneous traffic using this mathematical model. Deep learning can be used to represent heterogeneous patterns. This study proposes a generative model for Wi-Fi traffic that considers spatiotemporal characteristics using deep learning. The proposed model learns the heterogeneous traffic patterns from the AP-level measurement data without any assumptions and generates similar traffic patterns based on the data. The result shows that the difference between the sample generated by the proposed model and the collected data is up to 72.1% less than that reported in previous studies.https://www.mdpi.com/2079-9292/11/12/1848Wi-Fi usagetraffic modeltraffic patternsimulationgenerative model |
| spellingShingle | Jae-Min Lee Jong-Deok Kim A Generative Model for Traffic Demand with Heterogeneous and Spatiotemporal Characteristics in Massive Wi-Fi Systems Electronics Wi-Fi usage traffic model traffic pattern simulation generative model |
| title | A Generative Model for Traffic Demand with Heterogeneous and Spatiotemporal Characteristics in Massive Wi-Fi Systems |
| title_full | A Generative Model for Traffic Demand with Heterogeneous and Spatiotemporal Characteristics in Massive Wi-Fi Systems |
| title_fullStr | A Generative Model for Traffic Demand with Heterogeneous and Spatiotemporal Characteristics in Massive Wi-Fi Systems |
| title_full_unstemmed | A Generative Model for Traffic Demand with Heterogeneous and Spatiotemporal Characteristics in Massive Wi-Fi Systems |
| title_short | A Generative Model for Traffic Demand with Heterogeneous and Spatiotemporal Characteristics in Massive Wi-Fi Systems |
| title_sort | generative model for traffic demand with heterogeneous and spatiotemporal characteristics in massive wi fi systems |
| topic | Wi-Fi usage traffic model traffic pattern simulation generative model |
| url | https://www.mdpi.com/2079-9292/11/12/1848 |
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