LAUSR

According to state-of-the-art research, mobile network simulation is preferred over real testbeds, especially to evaluate communication protocols used in Opportunistic Networks (OppNet) or Mobile Ad hoc NETworks (MANET). The main reason behind it is the difficulty of performing experiments in real scenarios. However, in a simulation, a mobility model is required to define users’ mobility patterns. Trace-based models can be used for this purpose, but they are difficult to obtain, and they are not flexible or scalable. Another option is TRAce-based ProbabILiStic (TRAILS). TRAILS mimics the spatial dependency, geographic restrictions, and temporal dependency from real scenarios. In addition, with TRAILS, it is possible to scale the number of mobile users and simulation time. In this paper, we dive into the algorithms used by TRAILS to generate mobility graphs from real scenarios and simulate human mobility. In addition, we compare mobility metrics of TRAILS simulations, real traces, and another synthetic mobility model such as Small Worlds in Motion (SWIM). Finally, we analyze the performance of an implementation of the TRAILS model in computation time and memory consumption. We observed that TRAILS simulations represent the interaction among users of real scenarios with higher accuracy than SWIM simulations. Furthermore, we found that a simulation with TRAILS requires less computation time than a simulation with real traces and that a TRAILS graph consumes less memory than traces.