Research Article Open Access

Multiple Constraints for Ant Based Multicast Routing in Mobile Ad Hoc Networks

A. Sabari1 and K. Duraiswamy1
  • 1 ,
Journal of Computer Science
Volume 5 No. 12, 2009, 1020-1027

DOI: https://doi.org/10.3844/jcssp.2009.1020.1027

Submitted On: 30 October 2009 Published On: 31 December 2009

How to Cite: Sabari, A. & Duraiswamy, K. (2009). Multiple Constraints for Ant Based Multicast Routing in Mobile Ad Hoc Networks. Journal of Computer Science, 5(12), 1020-1027. https://doi.org/10.3844/jcssp.2009.1020.1027

Abstract

Problem statement: A Mobile Ad hoc Network (MANET) is one of the challenging environments for multicast. Since the associated overhead is more, the existing studies illustrate that tree-based and mesh-based on-demand protocols are not the best choice. The costs of the tree under multiple constraints are reduced by the several algorithms which are based on the Ant Colony Optimization (ACO) approach. The traffic-engineering multicast problem is treated as a single-purpose problem with several constraints with the help of these algorithms. The main disadvantage of this approach is the need of a predefined upper bound that can isolate good trees from the final solution. Approach: In order to solve the traffic engineering multicast problem which optimizes many objectives simultaneously this study offers a design on Ant Based Multicast Routing (AMR) algorithm for multicast routing in mobile ad hoc networks. Results: Apart from the existing constraints such as distance, delay and bandwidth, the algorithm calculates one more additional constraint in the cost metric which is the product of average-delay and the maximum depth of the multicast tree. Moreover it also attempts to reduce the combined cost metric. Conclusion: By simulation results, it is clear that our proposed algorithm surpasses all the previous algorithms by developing multicast trees with different sizes.

  • 1,346 Views
  • 1,688 Downloads
  • 1 Citations

Download

Keywords

  • MANETs multicast routing
  • ant colony optimization