Traffic Arrival Prediction for WiFi Network: A Machine Learning Approach

Ning Wang; Bo Li; Mao Yang; Zhongjiang Yan; Ding Wang

doi:10.1007/978-3-030-44751-9_40

Traffic Arrival Prediction for WiFi Network: A Machine Learning Approach

Ning Wang, Bo Li, Mao Yang, Zhongjiang Yan, Ding Wang

School of Electronics and Information

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Scopus citations

Abstract

At present, Wi-Fi plays a very important role in the fields of online media, daily life, industry, military and etc. Exactly predicting the traffic arrival time is quite useful for WiFi since the access point (AP) could efficiently schedule uplink transmission. Thus, this paper proposes a machine learning-based traffic arrival prediction method by using random forest regression algorithm. The results show that the prediction accuracy of this model is about 95%, significantly outperforming the linear prediction flow. Through prediction, resources can be reserved in advance for the arrival of data traffic, and the channel can be optimally configured, thereby achieving better fluency of the device and smoothness of the network.

Original language	English
Title of host publication	IoT as a Service - 5th EAI International Conference, IoTaaS 2019, Proceedings
Editors	Bo Li, Mao Yang, Zhongjiang Yan, Jie Zheng, Yong Fang
Publisher	Springer
Pages	480-488
Number of pages	9
ISBN (Print)	9783030447502
DOIs	https://doi.org/10.1007/978-3-030-44751-9_40
State	Published - 2020
Event	5th EAI International Conference on IoT as a Service, IoTaaS 2019 - Xi'an, China Duration: 16 Nov 2019 → 17 Nov 2019

Publication series

Name	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume	316 LNICST
ISSN (Print)	1867-8211

Conference

Conference	5th EAI International Conference on IoT as a Service, IoTaaS 2019
Country/Territory	China
City	Xi'an
Period	16/11/19 → 17/11/19

Keywords

Artificial intelligence
Big data
Machine learning
Random forest
Regression
Wi-Fi Network

Access to Document

10.1007/978-3-030-44751-9_40

Cite this

Wang, N., Li, B., Yang, M., Yan, Z., & Wang, D. (2020). Traffic Arrival Prediction for WiFi Network: A Machine Learning Approach. In B. Li, M. Yang, Z. Yan, J. Zheng, & Y. Fang (Eds.), IoT as a Service - 5th EAI International Conference, IoTaaS 2019, Proceedings (pp. 480-488). (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 316 LNICST). Springer. https://doi.org/10.1007/978-3-030-44751-9_40

Wang, Ning ; Li, Bo ; Yang, Mao et al. / Traffic Arrival Prediction for WiFi Network : A Machine Learning Approach. IoT as a Service - 5th EAI International Conference, IoTaaS 2019, Proceedings. editor / Bo Li ; Mao Yang ; Zhongjiang Yan ; Jie Zheng ; Yong Fang. Springer, 2020. pp. 480-488 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST).

@inproceedings{5b066347dde548999ea8da5f40aee961,

title = "Traffic Arrival Prediction for WiFi Network: A Machine Learning Approach",

abstract = "At present, Wi-Fi plays a very important role in the fields of online media, daily life, industry, military and etc. Exactly predicting the traffic arrival time is quite useful for WiFi since the access point (AP) could efficiently schedule uplink transmission. Thus, this paper proposes a machine learning-based traffic arrival prediction method by using random forest regression algorithm. The results show that the prediction accuracy of this model is about 95%, significantly outperforming the linear prediction flow. Through prediction, resources can be reserved in advance for the arrival of data traffic, and the channel can be optimally configured, thereby achieving better fluency of the device and smoothness of the network.",

keywords = "Artificial intelligence, Big data, Machine learning, Random forest, Regression, Wi-Fi Network",

author = "Ning Wang and Bo Li and Mao Yang and Zhongjiang Yan and Ding Wang",

note = "Publisher Copyright: {\textcopyright} 2020, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.; 5th EAI International Conference on IoT as a Service, IoTaaS 2019 ; Conference date: 16-11-2019 Through 17-11-2019",

year = "2020",

doi = "10.1007/978-3-030-44751-9_40",

language = "英语",

isbn = "9783030447502",

series = "Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST",

publisher = "Springer",

pages = "480--488",

editor = "Bo Li and Mao Yang and Zhongjiang Yan and Jie Zheng and Yong Fang",

booktitle = "IoT as a Service - 5th EAI International Conference, IoTaaS 2019, Proceedings",

}

Wang, N, Li, B , Yang, M, Yan, Z & Wang, D 2020, Traffic Arrival Prediction for WiFi Network: A Machine Learning Approach. in B Li, M Yang, Z Yan, J Zheng & Y Fang (eds), IoT as a Service - 5th EAI International Conference, IoTaaS 2019, Proceedings. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, vol. 316 LNICST, Springer, pp. 480-488, 5th EAI International Conference on IoT as a Service, IoTaaS 2019, Xi'an, China, 16/11/19. https://doi.org/10.1007/978-3-030-44751-9_40

Traffic Arrival Prediction for WiFi Network: A Machine Learning Approach. / Wang, Ning; Li, Bo ; Yang, Mao et al.
IoT as a Service - 5th EAI International Conference, IoTaaS 2019, Proceedings. ed. / Bo Li; Mao Yang; Zhongjiang Yan; Jie Zheng; Yong Fang. Springer, 2020. p. 480-488 (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST; Vol. 316 LNICST).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Traffic Arrival Prediction for WiFi Network

T2 - 5th EAI International Conference on IoT as a Service, IoTaaS 2019

AU - Wang, Ning

AU - Li, Bo

AU - Yang, Mao

AU - Yan, Zhongjiang

AU - Wang, Ding

PY - 2020

Y1 - 2020

N2 - At present, Wi-Fi plays a very important role in the fields of online media, daily life, industry, military and etc. Exactly predicting the traffic arrival time is quite useful for WiFi since the access point (AP) could efficiently schedule uplink transmission. Thus, this paper proposes a machine learning-based traffic arrival prediction method by using random forest regression algorithm. The results show that the prediction accuracy of this model is about 95%, significantly outperforming the linear prediction flow. Through prediction, resources can be reserved in advance for the arrival of data traffic, and the channel can be optimally configured, thereby achieving better fluency of the device and smoothness of the network.

AB - At present, Wi-Fi plays a very important role in the fields of online media, daily life, industry, military and etc. Exactly predicting the traffic arrival time is quite useful for WiFi since the access point (AP) could efficiently schedule uplink transmission. Thus, this paper proposes a machine learning-based traffic arrival prediction method by using random forest regression algorithm. The results show that the prediction accuracy of this model is about 95%, significantly outperforming the linear prediction flow. Through prediction, resources can be reserved in advance for the arrival of data traffic, and the channel can be optimally configured, thereby achieving better fluency of the device and smoothness of the network.

KW - Artificial intelligence

KW - Big data

KW - Machine learning

KW - Random forest

KW - Regression

KW - Wi-Fi Network

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U2 - 10.1007/978-3-030-44751-9_40

DO - 10.1007/978-3-030-44751-9_40

M3 - 会议稿件

AN - SCOPUS:85084004863

SN - 9783030447502

T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST

SP - 480

EP - 488

BT - IoT as a Service - 5th EAI International Conference, IoTaaS 2019, Proceedings

A2 - Li, Bo

A2 - Yang, Mao

A2 - Yan, Zhongjiang

A2 - Zheng, Jie

A2 - Fang, Yong

PB - Springer

Y2 - 16 November 2019 through 17 November 2019

ER -

Wang N, Li B , Yang M, Yan Z, Wang D. Traffic Arrival Prediction for WiFi Network: A Machine Learning Approach. In Li B, Yang M, Yan Z, Zheng J, Fang Y, editors, IoT as a Service - 5th EAI International Conference, IoTaaS 2019, Proceedings. Springer. 2020. p. 480-488. (Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST). doi: 10.1007/978-3-030-44751-9_40

Traffic Arrival Prediction for WiFi Network: A Machine Learning Approach

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Keywords

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