QoS based Route Optimization Model in MANET

Authors

  • K. Lakshmi  Assistant Professor, Department of Computer science and Engineering, G. Pullaiah College of Engineering and Technology, Kurnool, Andhra Pradesh, India

Keywords:

MANET, QOS, Routing Mechanism, Machine Learning Techniques.

Abstract

The primary objective of this paper is to develop specific evolutionary algorithms using Machine learning approach to enhance the selection of efficient and stable optimized routing path in MANET with a guarantee on QoS parameters. The primary QoS constraints considered include delay delay - jitter, bandwidth and packet loss rate for computing the possible network route. The essential characteristics of this routing process such as the accuracy, interpretability, robustness and versatility have been considered while calculating the workable routing path for MANETs. To attain this Machine learning techniques play vital, role in identify patterns such as optimized routing path and node-link failure detection which leads other than QoS and energy efficiency, security which attracts many researchers.

  1. To develop an innovative mechanism for the feasible path selection of the given network with a guarantee on QoS metrics.
  2. To identify the optimized routing patterns using Machine learning Approach to achieve an effective routing mechanism for dynamic, scalable networks.
  3. To Identify the Pattern for Node link failure among MANET by Machine Learning to handle the link failure in dynamic networks which establish the communication efficiency .
  4. Develop a secure authentication mechanism for improving the security in MANETs

References

  1. P Deepalakshmi and Dr.S.Radhakrishnan, "Ant colony based QoS routing algorithm for mobile ad hoc networks, International Journal of Recent Trends in Engineering, vol. 1, no. 1, May 2009, pp. 459-462.
  2. Asokan, R. Natarajan, A. Nivetha, "A swarm based distance vector routing to support multiple quality of service (QoS) metrics in MANETs, J. Comput. Sci., vol. 3, 2007, pp. 700-707.
  3. S Sethi and S. Udgata, "The efficient ant routing protocol for MANET, International Journal on Computer Science and Engineering, vol. 02, no. 07, 2010, pp. 2414-2420.
  4. S Kannan, T. Kalaikumaran, S. Karthik and V. Arunachalam, "Ant colony optimization for routing in mobile ad hoc networks, International Journal of Soft Computing, vol. 5, Iss. 6, 2010, pp. 223-228.
  5. BR.Sujatha and Dr. M.V. Sathyanarayana, "PBANT Optimized ant colony routing algorithm for manets, Global Journal of Computer Science and Technology, vol. 10, Iss. 2, April 2010, pp. 29- 34.
  6. P Venkata Krishna, V. Saritha, G. Vedha, A. Bhiwal and A. Bhiwal,"Quality of service enabled ant colony based multipath routing for mobile ad hoc networks, IET Commun., vol. 6, Iss. 1, 2012, pp. 76-83.
  7. E Bonabeau, M. Dorigo, and G. Theraulaz, Swarm Intelligence: From Natural to Arti_cial Systems. Oxford, U.K.: Oxford Univ. Press, 1999.
  8. J Bishop, "Stochastic searching networks," in Proc. 1st IEE Int. Conf.Artif. Neural Netw. (Conf.), 1989, pp. 329_331.
  9. R Poli, J. Kennedy, and T. Blackwell, "Particle swarm optimization,"Swarm Intell., vol. 1, no. 1, pp. 33_57, Jun. 2007, doi: https://doi.org/10.1007/s11721-007-0002-0.
  10. M. Dorigo, "Optimization, learning and natural algorithms," Ph.D. dissertation, Politecnico di Milano, Milan, Italy, 1992.
  11. H. Zhang, X. Wang, and D. Hogrefe, "A survey of location aware ant colony optimization routing protocols in MANETs," in Proc. 10th EAI Int.Conf. Bio-Inspired Inf. Commun. Technol. (BIONETICS), 2017.[Online].Available: http://bionetics.org/2017/show/accepted-papers
  12. C. S. Moreau, C. D. Bell, R. Vila, S. B. Archibald, and N. E. Pierce, "Phylogeny of the ants: Diversiation in the age of angiosperms," Science,vol. 312, no. 5770, pp. 101_104, 2006.
  13. B. Hlldobler and E. O. Wilson, The ANTS. Cambridge, MA, USA: Harvard Univ. Press, 1990.
  14. G.F. Oster and E. O. Wilson, Caste and Ecology in the Social Insects. Princeton, NJ, USA: Princeton Univ. Press, 1978.
  15. T. Flannery, Here on Earth: A Natural History of the Planet. New York, NY, USA: Grove, 2011.
  16. C.Anderson, G.Theraulaz, and J.-L. Deneubourg, "Self-assemblages in insect societies," Insectes Sociaux, vol. 49, no. 2, pp. 99_110, 2002.
  17. N. J. Mlot, C. A. Tovey, and D. L. Hu, "Fire ants self-assemble into water proof rafts to survive _oods," Proc. Nat. Acad. Sci. USA, vol. 108, no. 19, pp. 7669_7673, 2011.
  18. P. C. Foster, N. J. Mlot, A. Lin, and D. L. Hu, "Fire ants actively control spacing and orientation within self-assemblages," J. Experim. Biol., vol. 217, no. 12, pp. 2089_2100, 2014.
  19. N. Fujiwara-Tsujii, N. Yamagata, T. Takeda, M. Mizunami, and R. Yamaoka, "Behavioral responses to the alarm pheromone of the ant camponotus obscuripes (hymenoptera: Formicidae)," Zool. Sci., vol. 23, no. 4, pp. 353_358, 2006.
  20. H. Ahmed and J. Glasgow, "Swarm intelligence: Concepts, models and applications," School Comput., Queens Univ., Kingston, ON, Canada, Tech. Rep. 2012-585, 2012.

International Journal of Scientific Research in Science and Technology

Downloads

Published

2018-12-30

Issue

Volume 4, Issue 11, November-December-2018

Section

Research Articles

License

Copyright (c) IJSRST

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
K. Lakshmi, " QoS based Route Optimization Model in MANET, International Journal of Scientific Research in Science and Technology(IJSRST), Online ISSN : 2395-602X, Print ISSN : 2395-6011, Volume 4, Issue 11, pp.286-292, November-December-2018.