Mobility Models and traffic Pattern Generation Based Optimization of Reactive Protocols

A mobile ad-hoc network

Routing Protocol Classification

Classification of Mobility Models

Abstract: Mobile ad-hoc network is a collection of wireless mobile hosts forming a temporary network without the aid of any stand-alone infrastructure or centralized administration. Mobile Ad-hoc networks are self-organizing and self-configuring multi- hop wireless networks where, the structure of the network changes dynamically. This is mainly due to the mobility of the nodes. The primary objective of this paper is to study and investigate the performance measures of AODV, AOMDV and DSR routing protocols by using Nomadic Community Mobility Model, Random Direction Mobility Model and Pursue Mobility Model.A detailed simulation has been carried out in NS2. The metrics used for performance analysis are routing overhead, normalized routing load, avg end to end delay, throughput, routing cost and packet delivery ratio.

Introduction: 

Mobile networks can be classified into infrastructure networks and mobile ad hoc networks (MANET)(C-K Toh, 2002) according to their dependence on fixed infrastructures. In an infrastructure mobile network, mobile nodes have wired access points (or base stations) within their transmission range. In contrast, mobile ad-hoc networks are autonomously self-organized networks without infrastructure support. In a mobile ad hoc network, nodes move arbitrarily, therefore the network may experience rapid and unpredictable topology changes. Routing paths in MANETs potentially contain multiple hops, and every node in MANET has the responsibility to act as a router. Routing in MANET has been a challenging task ever since the wireless networks came into existence. The major reason for this is the constant change in network topology because of high degree of node mobility. A number of protocols have been developed to accomplish this task (Surayati, 2009) There exists various mobility models such as random way point, reference point group mobility model (RPGM), Manhattan mobility model, freeway mobility model, Gauss Mobility Model, Freeway Mobility Model, Gauss Markov Mobility Model,Nomadic Community Mobility Model, Random Direction Mobility Model, Pursue Mobility Model etc.(M. Sanchez, 2011)(Tracy Camp, 2002)

  Publication: (Authors: Deepak Karia and Vaibhav Godbole)

  In press of :
  International Journal Intelligence Engineering Informatics (Inderscience Publications)