The adequate and realistic modeling of the traffic load is a crucial step when building stochastic models of computer networks. With the increased availability of mobile devices performance evaluation of wireless networks has become more important. For a realistic load modeling in wireless networks the user mobility has an essential role.
The user movement patterns can either be generated synthetically or based on real data. While generating mobility models based on real data is much more elaborate, the resulting models have shown to be more realistic than synthetically generated ones. One issue with the creation of mobility models from real data stems from the granularity of the available data. Data that is recorded at WLAN access points does not contain information about exact movement patterns, only a mapping from users to access points is possible. However, data that allows for a more accurate determination of a user's position has been obtained from smaller user populations in the past. Different approaches for modeling user mobility have been developed that depend on the granularity of the data and on the goal of the analysis. A drawback of these approaches is that they cannot be used for different scenarios without problems.
Therefore, the project aims at developing a modular framework that meets the requirements of varying data granularity and different goals for an analysis. The mobility models that are part of the framework should be able to represent user mobility at different levels of abstraction and should be extendable to increase the level of detail easily.
Thus, the framework should provide a basis to compare the effect of different levels of abstraction for user mobility on performance measures.