Characterizing and Predicting Individual Traffic Usage of Mobile Application in Cellular Network.
UbiComp '18: The 2018 ACM International Joint Conference on Pervasive and Ubiquitous Computing Singapore Singapore October, 2018(2018)
摘要
The proliferation of smart devices prompts the explosive usage of mobile applications, which increases network traffic load. Characterizing the application level traffic patterns from an individual perspective is valuable for operators and content providers to make technical and business strategies. In this paper, we identify several typical traffic patterns and predict per-user traffic demand utilizing application usage dataset in cellular network. Our primary contributions are twofold: First, we novelly designed a three-stage model combining factor analysis and machine learning to extract the traffic patterns of individuals. By detecting the latent temporal structure of their application usage, users in the network are grouped into six typical patterns. Second, we implement a Wavelet-ARMA based model to forecast per-user application level traffic demand. The evaluation on real-world dataset indicates the model improves the prediction accuracy by 7 to 8 times compared with the benchmark solutions.
更多查看译文
关键词
Data Mining, Application Usage, Traffic Pattern, Traffic Prediction, Mobile Network
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络