Time series: theory and methods
Time series: theory and methods
On the self-similar nature of Ethernet traffic
ACM SIGCOMM Computer Communication Review - Special twenty-fifth anniversary issue. Highlights from 25 years of the Computer Communication Review
Statistical Digital Signal Processing and Modeling
Statistical Digital Signal Processing and Modeling
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
Time Series Models for Internet Data Traffic
LCN '99 Proceedings of the 24th Annual IEEE Conference on Local Computer Networks
Structural analysis of network traffic flows
Proceedings of the joint international conference on Measurement and modeling of computer systems
The changing usage of a mature campus-wide wireless network
Proceedings of the 10th annual international conference on Mobile computing and networking
Spatio-temporal modeling of traffic workload in a campus WLAN
WICON '06 Proceedings of the 2nd annual international workshop on Wireless internet
Singular spectrum analysis of traffic workload in a large-scale wireless lan
Proceedings of the 10th ACM Symposium on Modeling, analysis, and simulation of wireless and mobile systems
Prediction of noisy chaotic time series using an optimal radial basis function neural network
IEEE Transactions on Neural Networks
Long-term forecasting of Internet backbone traffic
IEEE Transactions on Neural Networks
Adaptare: Supporting automatic and dependable adaptation in dynamic environments
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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Network traffic load in an IEEE802.11 infrastructure arises from the superposition of traffic accessed by wireless clients associated with access points (APs). An accurate load characterization can be beneficial in modeling network traffic and addressing a variety of problems including coverage planning, resource reservation and network monitoring for anomaly detection. This study focuses on the statistical analysis of the traffic load measured in a campus-wide IEEE802.11 infrastructure at each AP. Using the Singular Spectrum Analysis approach, we found that the time-series of traffic load at a given AP has a small intrinsic dimension. In particular, these time-series can be accurately modeled using a small number of leading (principal) components. This proved to be critical for understanding the main features of the components forming the network traffic. Statistical analysis of leading components has demonstrated that even a few first components form the main part of the information. The residual components capture the small irregular variations, which do not fit in the basic part of the network traffic and can be interpreted as a stochastic noise. Based on these properties, we also studied contributions of the various components to the overall structure of the traffic load of an AP and its variation over time. Finally, we designed and evaluated the performance of a traffic predictor for the trend component, obtained by projecting the original time-series on the set of leading components.