Model T: A model for user registration patterns based on campus WLAN data

Abstract

We discuss the derivation of an empirical model for spatial registration patterns of mobile users in a campus wireless local area network (WLAN). Such a model can be very useful in a variety of simulation studies of the performance of mobile wireless systems, such as that of resource management and mobility management protocols. We base the model on extensive experimental data from a campus WiFi LAN installation. We divide the empirical data available to us into training and test data sets, develop the model based on the training set, and evaluate it against the test set.

The model shows that user registration patterns exhibit a distinct hierarchy, and that WLAN access points (APs) can be clustered based on registration patterns. Cluster size distributions are highly skewed, as are intra-cluster transition probabilities and trace lengths, which can all be modeled well by the heavy-tailed Weibull distribution. The fraction of popular APs in a cluster, as a function of cluster size, can be modeled by exponential distributions. There is general similarity across hierarchies, in that inter-cluster registration patterns tend to have the same characteristics and distributions as intra-cluster patterns. In this context, we also introduce and discuss the modeling of the disconnected state as an integral part of real traffic characteristics.

We generate synthetic traffic traces based on the model we derive. We then compare these traces against the real traces from the test set using a set of metrics we define. We find that the synthetic traces agree very well with the test set in terms of the metrics. We compare the derived model to a simple modified random waypoint model, and show that the latter is not at all representative of the real data. We also show how the model parameters can be varied to allow designers to consider ‘what-if’ scenarios easily. Finally we develop an extended version of Model T that uses an alternative modeling of relative popularity of APs and clusters, with certain generalization advantages, and evaluate its fidelity to the real data also, with positive results.