Morteza Mousavi Barroudi, The University of Melbourne, Australia

Various mobility models have been proposed to represent the motion behaviour of mobile nodes in the real world. Selection of the most similar mobility model to a given real world environment is a challenging issue which has a significant impact on the quality of performance evaluation of different network protocols. In this paper we propose a methodology for measurement of similarity between mobility models used in mobile networks simulation and real world mobility scenarios with different transportation modes. We explain our mobility metrics we have used for analysis of motion behavior of mobile nodes and a pre-processing method which makes our trajectories suitable for extraction and calculation of these metrics considering shape of the road networks and GPS noise. Then we use a feature selection method to find the most discriminative features which are able to distinguish between trajectories with different transportation modes using a supervised learning and feature ranking method. Subsequently, using our selected feature space we perform Fuzzy C-means Clustering to find the degree of similarity between each of our mobility models and real world trajectories with different transportation modes. Our methodology can be used to select the most similar mobility model suitable for simulation of mobile network protocols (such as DTN and MANETs protocols) in a particular real world area.

Mobility Models, Similarity Analysis, Transportation Modes, Mobile Networks