Paper Details: Downloads: 952
Serial Number: P1141038307
Title: Applying Optimized Mobility Model for Reinforcement Learning Based Routing Algorithm for Mobile Ad Hoc Network
Authors: Shrirang Ambaji Kulkarni and G. Raghavendra Rao
Abstract: Mobile ad hoc network are characterized by nodes which are moving continuously. This characterization of node movement is usually done by mobility models which mimic the movement of nodes in real world. Routing algorithms play a vital role in the working of ad hoc networks. Ad hoc networks are constrained by congestion, limited bandwidth, energy and security. To achieve performance optimization under these dynamic scenarios, we consider a reinforcement learning routing algorithm and compare its performance with traditional routing algorithms like dynamic source routing and ad-hoc on demand distance. Most of the simulation based studies involve Random Waypoint Model which is far away from realism. Also most of the research in mobility characterization has been for individual nodes rather than group of nodes. Thus we propose a novel realistic group mobility model based on City Section and achieve the performance optimization of reinforcement learning based routing algorithm in the presence of increasing traffic and high speed mobility conditions.
Keywords: Mobile ad hoc networks, mobility models, reinforcement learning, routing protocols, optimization
Journal/Conference: International Journal of Computer Networks and Internet Research
Volume: 10
Issue: 1
Submission Date: 9/11/2010 12:00:00 AM
Review Date: 9/20/2010 12:00:00 AM
Publishing Date: 10/2/2010 12:00:00 AM
Article Downloads: 952
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