CFP last date
20 December 2024
Reseach Article

Swarm Intelligence based Energy Efficient Routing Protocol for Wireless Ad-hoc Networks

by K Ashok Babu, D Sreenivasa Rao, S. Lakshminarayana
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 62 - Number 2
Year of Publication: 2013
Authors: K Ashok Babu, D Sreenivasa Rao, S. Lakshminarayana
10.5120/10055-4644

K Ashok Babu, D Sreenivasa Rao, S. Lakshminarayana . Swarm Intelligence based Energy Efficient Routing Protocol for Wireless Ad-hoc Networks. International Journal of Computer Applications. 62, 2 ( January 2013), 34-39. DOI=10.5120/10055-4644

@article{ 10.5120/10055-4644,
author = { K Ashok Babu, D Sreenivasa Rao, S. Lakshminarayana },
title = { Swarm Intelligence based Energy Efficient Routing Protocol for Wireless Ad-hoc Networks },
journal = { International Journal of Computer Applications },
issue_date = { January 2013 },
volume = { 62 },
number = { 2 },
month = { January },
year = { 2013 },
issn = { 0975-8887 },
pages = { 34-39 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume62/number2/10055-4644/ },
doi = { 10.5120/10055-4644 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:10:38.204999+05:30
%A K Ashok Babu
%A D Sreenivasa Rao
%A S. Lakshminarayana
%T Swarm Intelligence based Energy Efficient Routing Protocol for Wireless Ad-hoc Networks
%J International Journal of Computer Applications
%@ 0975-8887
%V 62
%N 2
%P 34-39
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Mobile Ad Hoc Networks are communication networks built up of a collection of mobile devices which can communicate through wireless connections. Mobile Ad-hoc network (MANET) has emerged as the self organized wireless interconnection for the various applications in random topology. However, achieving reliable multicast transmission in MANET is crucial due to the change in network topology caused by the node mobility. Wireless Networks are characterized by having specific requirements such as limited energy availability and reduced processing power. In this paper deals with the inability of the network to recover in case of failure networks, to reduce the maintenance overhead, increase the path stability, reducing the congestion in Mobile Ad-hoc network and with the inability of the network to recover in case of power problem in wireless network . Ant based routing protocols can add a significant contribution to assist in the maximisation of the network life-time, but this is only possible by means of an adaptable and balanced algorithm that takes into account the Wireless Sensor networks main restrictions. We are introducing a new concept of two ants, one acting as load agent and another as strategy agent to ensure better performance. The strategy agent is software acting as a processor which controls and guide the load agents as forward ants and backward ants. We are using a Backpressure technique for network activities as link failure and restoration of link information. We carry out these simulation results using NS 2. 34.

References
  1. AK Daniel, R Singh "Swarm Intelligence Based Multicast Routing and Bandwidth Management Protocol for AD-hoc Wireless Network Using Backpressure Restoration".
  2. Tiago Camilo, Carlos Carreto, Jorge Sá Silva, Fernando Boavida" An Energy-Efficient Ant-Based Routing Algorithm for Wireless Sensor Networks".
  3. E. Bonabeau, M. Dorigo, and G. Theraulaz. Swarm intelligence: from natural to artificial intelligence. Oxford University Press, 1999. ISBN 0-19-513158-4.
  4. J. Broch, D. A. Maltz, D. B. Johnson, Y. -C. Hu, and J. Jetcheva. A performance comparison of multihop wireless ad hoc network routing protocols. Proceedings of the Fourth Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom'98), pages 85-97, 1998.
  5. M. Dorigo and G. Di Caro. The ant colony optimization meta-heuristic. In D. Corne, M. Dorigo, and F. Glover, editors, New Ideas in Optimization, pages 11–32. McGraw Hill, London, 1999.
  6. C. E. P. (Editor). Ad Hoc Networking. Addison-Wesley, 2001. ISBN 0-201-30976-9.
  7. K. Fall and K. Varadhan. The ns Manual, Nov 2000.
  8. T. White, system 8. Pagurek, and F. Oppacher, "ASGA: improving the ant By integration with genetic algorithms", Proc Third GeneticProgramming Conference, July, 1998, pp. 610-617.
  9. M. Dorigo, V. Maniezzo, and A. Colorni, "The ant system: optimization by a colony of cooperating agents", IEEE Transactions on Systems,Man, and Cybernetics, Part B, vol. 26, no. I, pp. 29-41, 1996
  10. Zhang, Y. , Kuhn, L. , Fromherz, M. , „Improvements on Ant Routing for Sensor Networks". In: Ants 2004, Int. Workshop on Ant Colony Optimization and Swarm Intelligence, Sept. 2004
  11. Singh, G. , Das, S. Gosavi, S. , Pujar, S. , "Ant Colony Algorithms for Steiner Trees: An Application to Routing in Sensor Networks", Recent Developments in Biologically InspiredComputing, Eds. L. N. de Castro, F. J. von Zuben, Idea Group Publishing, pp. 181-206, 2004
  12. Zuniga, M. Z. ; Krishnamachari, B. "Integrating Future Large-Scale Wireless Sensor Networks with the Internet" - Department of Electrical Engineering, UNiversity of SouthernCalifornia, 2002
  13. Alonso, J. , Dunkels, A. , Voigt. T, „Bounds on the energy consumption of routings in wireless sensor nodes" WiOpt'04: Modeling and Optimization in Mobile, Ad Hoc and WirelessNetworks, Cambridge, UK, March 2004.
Index Terms

Computer Science
Information Sciences

Keywords

MANET congestion energy QoS