BonnMotion 4 – Taking Mobility Generation to the Next Level

Abstract

Human mobility plays an important role in multiple research areas, ranging from virus spread research to network performance analysis. As real-world campaigns tend to be rather time consuming and expensive, they are often used to extract statistical features characterizing human mobility, which are then used as basis to create mobility models. For the implementation of such models and the subsequent simulative generation of movement traces, various tools exist. One such tool, focusing on the mobility modeling aspect of network performance analysis, is BonnMotion (BM).In this paper, we introduce new features to BM which increase the overall usability of the framework, simplify the implementation of new models, and improve the trace generation performance by introducing parallel processing capabilities. In addition, the introduced features are used to implement two additional movement models: The Sedentary Random Waypoint model and the Working Day Model. Furthermore, we exemplarily evaluate both, the impact of our enhancements, and the newly added models.