Characterizing flows in large wireless data networks

Several studies have recently been performed on wireless university campus networks, corporate and public networks. Yet little is known about the flow-level characterization in such networks. In this paper, we statistically characterize both static flows and roaming flows in a large campus wireless network using a recently-collected trace. For static flows, we take a two-tier approach to characterizing the flow arrivals, which results a Weibull regression model. We further discover that the static flow arrivals in spatial proximity show strong similarity. As for roaming flows, they can also be well characterized statistically.We explain the results by user behaviors and application demands, and further cross-validate the modeling results by three other traces. Finally, we use two examples to illustrate how to apply our models for performance evaluation in the wireless context.