Reengineering MANET Routing using Ant Colony Optimization: Modelling and Performance Study
Author(s) -
Bright Selorm,
Michael Asante,
Benjamin Hayfron-Acquah,
K. Ebenezer
Publication year - 2020
Publication title -
international journal of computer applications
Language(s) - English
Resource type - Journals
ISSN - 0975-8887
DOI - 10.5120/ijca2020920372
Subject(s) - computer science , ant colony optimization algorithms , business process reengineering , routing (electronic design automation) , operations research , artificial intelligence , computer network , manufacturing engineering , lean manufacturing , engineering
A wireless network topology that comprise heterogeneous routing paths without the supported of any preliminary network infrastructure is known as a Mobile Ad Hoc Network. Therefore, the nodes have the capacity to forward packets to nodes. Furthermore, MANET properties, namely; dynamic topology, nodal mobility, provide a large degree of freedom and the ability to self-organize give them an urge over other network architectures and topologies. For that matter, designing and developing secure routing algorithms becomes a daunting task for the researcher. To this end, this paper seek to compare preexisting and proposed routing algorithm for MANET based on the mechanism of the ant system , hence Ant Colony Optimization frame would be adopted. It is notable that MANET bandwidth, radio propagation, energy supply, etc. Different MAC protocols have proposed for adhoc networks. In this research, a new algorithm based on the Ant Colony Optimization algorithm framework is proposed. The proposed Ant Colony Optimization algorithm known as Optimized Multicast Routing Algorithm was implemented with the aid of MANET simulation in Mathlab and a performance comparison was carried out. The study compared, the propriety protocol AODV, with the proposed algorithm. It was observed the proposed protocol outperformed AODV under the quantitative metrics used. General Terms Routing Protocols, Energy-Efficiency, Wireless Routing. Ant Colony Optimization.
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