A study of the Destination Guided Mobility models.

Mobility models play a critical role in the simulation studies of Mobile Ad hoc Networks (MANETs). They greatly influence the performance of MANET routing protocols. For MANET simulations, random mobility models have been used in nearly all research studies in the past. In recent times, several studies have criticised the use of random mobility models in the performance studies of MANETs for the lack of realism in modelling mobility. Therefore, questions have been raised regarding the credibility of MANET simulation studies. Realism and simplicity are two attractive properties of mobility models achieving both together in modelling mobility has been a challenging task. Recently, a framework of mobility models called Destination Guided Mobility (DGM) models for MANETs with a basic software tool was proposed. This framework can be used to develop several simple DGM models with improved realism. This thesis is primarily interested in studying DGM models for their suitability in modelling mobility in various MANET scenarios. Our study requires a suitable simulation testbed for DGM models. Designing such a tool, referred to as DGMGen, with suitable functionality to study DGM models is the secondary objective of this thesis. More specifically, after the design and implementation of DGMGen, we study: i) the generality of the DGM models by modelling different real world scenarios ii) the connectivity analysis of three basic DGM models in comparison with the widely used Random Waypoint (RWP) mobility model iii) how to model a real life scenario using DGM models, based on the trace collected from that scenario and iv) the impact of DGM models on the Ad hoc On-demand Distance Vector (AODV) routing protocol using NS2. Our study shows that i) the DGM framework is powerful in capturing various MANET scenarios simply and more accurately, ii) DGM models confirm higher level connectivity prevailed in most real world scenarios, iii) DGM models can generate approximately the similar trace based on the insights of a real trace