@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",
}