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Erschienen in:
2020 | OriginalPaper | Buchkapitel
Traffic Arrival Prediction for WiFi Network: A Machine Learning Approach
verfasst von : Ning Wang, Bo Li, Mao Yang, Zhongjiang Yan, Ding Wang
Erschienen in: IoT as a Service
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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.