Integrating Granger Causality and Vector Auto-Regression for Traffic Prediction of Large-Scale WLANs

• Zheng Lu,
• Chen Zhou,
• Jing Wu,
• Hao Jiang,
• Songyue Cui,

Vol. 10, No. 1, January 30, 2016
10.3837/tiis.2016.01.008, Download Paper (Free):

• wireless local area network
• traffic prediction
• Granger Causality
• Vector auto-regression

Abstract

Flexible large-scale WLANs are now widely deployed in crowded and highly mobile places such as campus, airport, shopping mall and company etc. But network management is hard for large-scale WLANs due to highly uneven interference and throughput among links. So the traffic is difficult to predict accurately. In the paper, through analysis of traffic in two real large-scale WLANs, Granger Causality is found in both scenarios. In combination with information entropy, it shows that the traffic prediction of target AP considering Granger Causality can be more predictable than that utilizing target AP alone, or that of considering irrelevant APs. So We develops new method - Granger Causality and Vector Auto-Regression (GCV AR), which takes APs series sharing Granger Causality based on V ector Auto-regression (VAR) into account, to predict the traffic flow in two real scenarios, thus redundant and noise introduced by multivariate time series could be removed. Experiments show that GCVAR is much more effective compared to that of traditional univariate time series (e.g. ARIMA, WARIMA). In particular, GCVAR consumes two orders of magnitude less than that caused by ARIMA/WARIMA.

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