  One vision of future wireless networks is that they will be deeply integrated
and embedded in our lives and will involve the use of personalized mobile
devices. User behavior in such networks is bound to affect the network
performance. It is imperative to study and characterize the fundamental
structure of wireless user behavior in order to model, manage, leverage and
design efficient mobile networks. It is also important to make such study as
realistic as possible, based on extensive measurements collected from existing
deployed wireless networks.
  In this study, using our systematic TRACE approach, we analyze wireless
users' behavioral patterns by extensively mining wireless network logs from two
major university campuses. We represent the data using location preference
vectors, and utilize unsupervised learning (clustering) to classify trends in
user behavior using novel similarity metrics. Matrix decomposition techniques
are used to identify (and differentiate between) major patterns. While our
findings validate intuitive repetitive behavioral trends and user grouping, it
is surprising to find the qualitative commonalities of user behaviors from the
two universities. We discover multi-modal user behavior for more than 60% of
the users, and there are hundreds of distinct groups with unique behavioral
patterns in both campuses. The sizes of the major groups follow a power-law
distribution. Our methods and findings provide an essential step towards
network management and behavior-aware network protocols and applications, to
name a few.
