Evaluation of short-term traffic forecasting algorithms in wireless networks

Our goal is to characterize the traffic load in an IEEE 802.11 infrastructure. This can be beneficial in many domains, including coverage planning, resource reservation, and network monitoring for anomaly detection, and producing more accurate simulation models. We conducted an extensive measurement study of wireless users on a major university campus using the IEEE 802.11 wireless infrastructure. This paper proposes and evaluates several traffic forecasting algorithms based on various traffic models that employ the periodicity, recent traffic history, and flow-related information. Finally, it discusses the impact of time-scale and history on the prediction accuracy