Asian Journal of Computer Science and Technology (AJCST)
Comparative Performance Evaluation of On-Demand Multicast Routing Protocols for MANET
Author : K. KavithaVolume 7 No.1 Special Issue:November 2018 pp 123-129
Abstract
A Mobile Ad hoc NETwork (MANET) is a collection of wireless nodes communicating with each other in the absence of any infrastructure. Each device in a MANET is free to move independently in any direction, and will therefore change its links to other devices frequently. Each must forward traffic unrelated to its own use, and therefore be a router. The primary challenge in building a MANET is equipping each device to continuously maintain the information required to properly route traffic. Such networks may operate by themselves or may be connected to the larger Internet. In this paper, we compare the performance of On-Demand Multicast Routing Protocol (ODMRP), Adaptive demand driven Multicast Routing Protocol (ADMR) With Efficient Geographic Multicast Routing Protocol (EGMP) under different mobility models such as Random Way Point Model, Manhattan Model and Random Drunken Model. Using these models, performance metrics such as Packet Delivery Ratio, End-to-End Delay and Control Overhead are evaluated. ODMRP dynamically builds the route and manages the group membership. In ADMR, Multicast routing state is dynamically established and maintained only for active groups and only in nodes located between multicast senders and receivers. ADMR detects the high mobility without the use of GPS or other positioning system. EGMP supports a zone-based scheme to efficiently handle the two-tier membership management, and takes advantage of the membership management structure to efficiently track the locations of all the group members. The simulation result shows that the throughput of ADMR is higher than that of ODMRP and EGMP at high mobility and EGMP is high at low mobility. End to end delay and control overhead of EGMP is higher than that of ODMRP and ADMR.
Keywords
MANET, ODMRP, ADMR, EGMP, Random Way Point Model, Random Drunken Model, Manhattan Model
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