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 character-
ize 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 fur-
ther 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.