MobiSim: A Framework for Simulation of Mobility Models in Mobile Ad-Hoc Networks

Abstract

A Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes forming a self-configuring network without using any existing infrastructure. Since MANETs are not currently deployed on a large scale, research in this area is mostly simulation based. Among other simulation parameters, the mobility model plays a very important role in determining the protocol performance in MANET. Thus, it is essential to study and analyze various mobility models and their effect on MANET protocols. In this paper we introduce a new framework for simulation of mobility models in mobile Ad-Hoc networks. This simulator can generate mobility traces in various mobility models. The mobility traces can be customized for different network simulators using XML and text output formats. User friendly graphical interface and batch processing ability makes our simulator one of the most efficient and useful mobility simulators in this field of research. We also propose some new features and parameters in mobility models to make the behavior of our simulator supported mobility models more similar to real world mobile node motions and fix some problems in last proposed methods to generate mobility models.

Geometry of interest (GOI): spatio-temporal destination extraction and partitioning in GPS trajectory data

“…These trajectories could be collected by social networks (Cho et al, 2011), sensor networks (Ji and Zha, 2004), RFIDs (Kourogi et al, 2006), WI-FI (Song et al, 2006), simulators (Mousavi et al, 2007), internet of things (Macagnano et al, 2014), and cellular networks (Si et al, 2010). Among all of these kinds of trajectories, our work is focused on the trajectories collected by GPS sensors.…”

Measuring Similarity Between Mobility Models and Real World Motion Trajectories

“…To be able to preform similarity analysis using the selected feature set, ({1; 3; 4; 9}), we generated one mobility trace for each of the following mobility models that we needed to analyse: (RW, RWP, RPGM, MAN, and LW) using our previously developed mobility simulator [2]. Each of the mobility traces include 50 mobile nodes moving in an area of 10K X 10K meters moving for 10000 sample times.…”

“…Maximum pause times for RWP and Manhattan have set to 10 sample times and minimum pause time to 1 sample time. We used a regular Manhattan map for the Manhattan mobility model with 50 equidistant intersections in the simulation area [2]. For Levy Walk mobility model, we set the maximum pause time to 10 and minimum pause time to 1 sample times.…”

“…These simulators provide facilities for simulation of motion of mobile nodes in the plain simulation area using mobility models such as Randowm Waypoint, Random Walk, Brownian Motion [1], Markovian [2], and RPGM [3], [4], [2]. One question here is which of these highly used mobility models perform more similar to real world motion scenarios?…”

“…We use this fuzzy membership as a measure for the similarity between our samples (trajectories with different transportation modes) and clusters of mobility models. Before, performing the clustering we extract features from 5 classes of mobility models, (Random Waypoint (RWP), Random Walk (RW), Levy Walk(LW), Manhattan (MAN), and RPGM [6], [3], [4], [2], [5]) as representatives of mobility models. We also extract the features of our transportation mode trajectories.…”