Frequency synchronization enhancement in wireless sensor network using cheetah chase algorithm

  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract

    Frequency synchronization is a cutting edge framework for any distributed systems. Wireless sensor networks have risen as an imperative and promising exploration territory in the current years. Frequency synchronization is an imperative for some, sensor organize applications that require extremely exact mapping of assembled sensor information with the frequency of the occasions happened. Biologically inspirited, innovative swarm intelligence algorithms are the most unique algorithms for enhancement. In this proposed work, new population based nature-impelled metaheuristic optimization algorithm, named Cheetah Chase Algorithm (CCA), is presented for upgrading the frequency synchronization in the distributed environment.



  • Keywords

    Frequency Synchronization; Wireless sensor network; Cheetah Chase Algorithm.

  • References

      [1] Alazzawi, L. and A. Elkateeb. 2008. Performance evaluation of the WSN routing protocols scalability. J. Comput. Syst. Netw. Commun., 2008: 481046-481054.

      [2] Almshreqi, A.M.S., B.M. Ali, M.F.A. Rasid, A. Ismail and P. Varahram, 2012. An improved routing mechanism using bio-inspired for energy balancing in wireless sensor networks. Proceedings of the International Conference on Information Networking, Feb. 1-3, IEEE Xplore Press, pp: 150-153.

      [3] Altringham JD (1996) Bats: Biology and Behaviour, Oxford Univesity Press.

      [4] Bains, V. and K. Sharma, 2012. Ant colony-based routing in wireless sensor networks. Int. J. Electron. Comput. Sci. Eng., 1: 2516-2524.

      [5] Dhurandher, S.K., S. Misra, M.S. Obaidat and N. Gupta, 2008. QDV: A quality-of-security-based distance vector routing protocol for wireless sensor networks using ant colony optimization. Proceedings of the IEEE International Conference on Wireless and Mobile Computing Networking and Communications, Oct. 12-14, IEEE Xplore Press, pp: 598-602.

      [6] Husna Jamal Abdul Nasir, Ku Ruhana Ku-Mahamud and Eiji Kamioka “Ant Colony Optimization Approaches in Wireless Sensor Network: Performance Evaluation” Journal of Computer Science 2017, 13 (6): 153.164

      [7] Jangra, A., A. Awasthi and V. Bhatia. 2013. A study on swarm artificial intelligence. Int. J. Adv. Res. Comput. Sci. Software Eng., 3: 259-263.

      [8] Kaur, N. and S. Monga, 2014. Comparisons of wired and wireless networks: A review. Int. J. Adv. Eng. Technol., 5: 34-35.

      [9] Maraiya, K., K. Kant and N. Gupta, 2011. Wireless sensor network: A review on data aggregation. Int. J. Scientific Eng. Res., 2: 1-6.

      [10] Ranganathan, P. and K. Nygard, 2010. Time synchronization in wireless sensor networks: A survey. Int. J. UbiComp, 1: 92-102.

      [11] Richardson P (2008) Bats. Natural History Museum, London.

      [12] Singh, A. and S. Behal, 2013. Ant colony optimization for improving network lifetime in wireless sensor networks. Int. J. Eng. Sci., 8: 1-12.

      [13] Tyrrell.A and G. Auer. Imposing a reference timing on firefly synchronization in wireless networks. In Proceedings of the 65th IEEE Vehicular Technology Conference (VTC 2007-Spring), pages 222–226, Dublin, Ireland, April 2007.

      [14] Tyrrell.A and G. Auer. Biologically inspired intercellular slot synchronization. EURASIP Journal on Wireless Communications and Networking, ID 854087:1–12, January 2009.

      [15] Xin-She Yang and Amir H. Gandomi, Bat Algorithm: A Novel Approach for Global Engineering Optimization, Engineering Computations, Vol. 29, Issue 5, pp. 464--483 (2012).

      [16] X.-S. Yang (Ed.): Bat Algorithm and Cuckoo Search: A Tutorial Artif. Intell., Evol. Comput. and Metaheuristics, SCI 427, pp. 421–434. _c Springer-Verlag Berlin Heidelberg 2013.

      [17] Yang, X.S.: Nature-Inspired Metaheuristic Algorithms, 2nd edn. Luniver Press, UK (2010).

      [18] Yang X-S (2010) A new metaheuristic bat-inspired algorithm, in: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010) (Eds. Cruz C., Gonzalez J., Krasnogor N., and Terraza G.), Springer, SCI 284, pp 65-74.

      [19] Goudhaman.M, “Cheetah Chase Algorithm (CCA): A Nature inspired metaheuristic algorithm” IJET, International Journal of Engineering & Technology, VOLUME 7, ISSUE 3, January/2018 PAGE NO: 1804-1811

      [20] Goudhaman.M, Vanathi. N, Sasikumar.S, (2018), Semantic Approach for Dynamic Shortest Path Problem (SPP) By Cheetah Chase Algorithm (CCA), IAETSD Journal For Advanced Research In Applied Sciences Volume 5, Issue 8, August/2018 ISSN NO: 2394-8442, PAGE NO:229-237, UGC Indexed Journal - August 2018.






Article ID: 30209
DOI: 10.14419/ijet.v9i1.30209

Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.