The widespread use of user-carried devices with short-range communication leads to networks characterized by high dynamics, sporadic connectivity, and strong partitioning. In such networks, connectivity between mobile nodes is strongly influenced by sociological aspects. To enable the evaluation of mobile applications which communicate in such networks, we require an appropriate mobility model.

In this thesis, we have designed and implemented a mobility model which focuses on the simulation of social context. It takes an arbitrary weighted social network as input and reflects its structural properties in its mobility scheme. Based on this approach, our model allows to integrate recent advances in the research of complex social networks. In addition, we focus on the simulation of different typical human characteristics such as the periodical reappearance at preferred locations and movement in groups. Furthermore, our model allows the integration of mobility models which concentrate on geographical aspects such as modeling obstacles or realistic movement between locations.

We provide experimental results that show that our model reflects the input social network with an accuracy of up to 99%. In addition, we show that our model captures the characteristics measured in traces of human mobility, which shows the validity of our approach. The generalizational character of our model enables the fast integration of future research results in the areas of human mobility and complex social networks.