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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Wang, M., et al.: Machine learning for networking: workflow, advances and opportunities. IEEE Netw. 32(2), 92–99 (2018)
Jiang, J., et al.: CFA: a practical prediction system for video QoE optimization. In: Usenix Conference on Networked Systems Design and Implementation USENIX Association (2016)
Fadlullah, Z., et al.: State-of-the-art deep learning: evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun. Surv. Tutorials 19, 2432–2455 (2017)
Kato, N., et al.: The deep learning vision for heterogeneous network traffic control: proposal, challenges, and future perspective. IEEE Wireless Commun. 24, 146–153 (2017)
Mao, H., et al.: Resource management with deep reinforcement learning. In: The 15th ACM Workshop. ACM (2016)
Acknowledgement
This work was supported in part by the National Natural Science Foundations of CHINA (Grant No. 61771390, No. 61871322, No. 61771392, No. 61271279, and No. 61501373), the National Science and Technology Major Project (Grant No. 2016ZX03001018-004), and Science and Technology on Avionics Integration Laboratory (20185553035).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Wang, N., Li, B., Yang, M., Yan, Z., Wang, D. (2020). Traffic Arrival Prediction for WiFi Network: A Machine Learning Approach. In: Li, B., Zheng, J., Fang, Y., Yang, M., Yan, Z. (eds) IoT as a Service. IoTaaS 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 316. Springer, Cham. https://doi.org/10.1007/978-3-030-44751-9_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-44751-9_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44750-2
Online ISBN: 978-3-030-44751-9
eBook Packages: Computer ScienceComputer Science (R0)