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An Evolutionary Intuitionistic Fuzzy K-means Clustering Approach based Cluster Head Selection in MANET

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2017
S. P. Swornambiga, Antony Selvadoss Thanamani

S P Swornambiga and Antony Selvadoss Thanamani. An Evolutionary Intuitionistic Fuzzy K-means Clustering Approach based Cluster Head Selection in MANET. International Journal of Computer Applications 170(4):15-21, July 2017. BibTeX

	author = {S. P. Swornambiga and Antony Selvadoss Thanamani},
	title = {An Evolutionary Intuitionistic Fuzzy K-means Clustering Approach based Cluster Head Selection in MANET},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2017},
	volume = {170},
	number = {4},
	month = {Jul},
	year = {2017},
	issn = {0975-8887},
	pages = {15-21},
	numpages = {7},
	url = {},
	doi = {10.5120/ijca2017914820},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Recent technological advances in sensors, low-power microelectronics and miniaturization, and wireless networking enabled the design and proliferation of Mobile Adhoc Networks capable of autonomously monitoring and controlling environments. One of the most promising problems existing is efficient data transmission with less resource utilization between cluster head and base station. This paper handles the most influencing factor to obtain such efficiency is energy consumption, density and distance. This proposed work implements two stages in the Clustering phase the sensor nodes are clustered using the intuitionistic fuzzy K-means clustering using the membership and non-membership value of each factors taken into the account. Once clustering phase is over then the cluster head is selected based on the highest fitness function obtained using the genetic algorithm here the node with highest energy consumption and lower distance from base station and neighboring nodes are considered as optimal cluster head. The data packets are aggregated and transferred using the cluster head to the base station. During each round the cluster in reframed using intuitionistic fuzzy k-means. The result shows that the proposed method well performed in the case of uncertainty in cluster head selection.


  1. C. R. Lin and M. Gerla, “Adaptive Clustering for Mobile Wireless Networks,” IEEE JSAC, vol. 15, Sept. 1997, pp. 1265–75.
  2. R. Agarwal and D. Motwani, “Survey of clustering algorithms for MANET,”
  3. M. Chatterjee, S. Sas, and D. Turgut, “An on-demand weighted clustering algorithm (WCA) for ad hoc networks,” in Proceedingsf the IEEE Global Telecommunications Conference(GLOBECOM ’00), 2000.
  4. P.Chatterjee, “Trust based clustering and secure routing scheme for mobile ad hoc networks,” International Journal of Computer Networks and Communication, vol. 1, no. 2, pp. 84–97, 2009.
  5. S. Chinara and S. K. Rath, “A survey on one-hop clustering algorithms in mobile ad hoc networks,” Journal of Network andSystems Management, vol. 17, no. 1-2, pp. 183–207, 2009.
  6. C.-L. Fok, G.-C. Roman, and C. Lu, “Rapid development and flexible deployment of adaptive wireless sensor network applications,” in Proceedings of the 25th IEEE International Conference on Distributed Computing Systems (ICDCS ’05), pp. 653–662, June 2005.
  7. C. Liu, C. Lee, L.ChunWang, "Distributed clustering algorithms for data gathering in wireless mobile sensor networks", Elsevier Sci. J. Parallel DistribComput., Vol.67, 2007, pp.l187 -1200
  8. W. Heinzelman, A. Chandrakasan and H. Balakrishnan., "An Application -Specific Protocol Architecture for Wireless Microsensor Networks", IEEE Trans. Wireless Communications, Vol. 1, No.4, October 2002, pp.660-670.
  9. W. Heinzelman, Application - Specific Protocol Architectures for Wireless Networks, Ph.D Thesis, Massachusetts Institute of Technology, June 2000.
  10. G. Smaragdakis, I. Matta, A. Bestavros, SEP: A Stable Election Protocol for clustered heterogeneous wireless sensor networks, In Second International Workshop on Sensor and Actor Network Protocols and Applications (SANPA), 2004
  11. D. J. Baker and A. Ephremides. A Distributed Algorithm for Organizing Mobile Radio Telecommunication Network. In proceedings of the 2nd International Conference on Distributed Computer Systems, page 476 - 483, April 1981.
  12. D. J. Baker and A. Ephremides. The Architectural Organization of a Mobile Radio Network via a Distributed Algorithm. IEEE Transactions on Communication, COM - 29 11:1694, 1701. November 1981.
  13. Ratish Agrawal, Dr. Mahesh Motwani, “Survey of Clustering Algorithm for Mobile Ad hoc Network”, IJCSE, Vol. 1 Issue 2, pp. 98-104, 2009
  14. M. Gerla and J. T. Tsai, “Multicluster, Mobile, Multimedia Radio Network. Wireless Network,” 1995
  15. G. Chen, F. Nocetti, J. Gonzalez, and I. Stojmenovic, “Connectivity based k - hop clustering in wireless network,” In proceeding of International Conference on System Science, Vol. 7, pp. 188.3, 2002
  16. P. Basu, N. Khan, and T.D.C. Little, “A Mobility Based Metric for Clustering in Mobile Ad Hoc Networks”, In proceeding of IEEE ICDCSW, pp. 413 - 18, Apr. 2001
  17. Ching-Wen Huang, Kuo-Ping Lin, Ming-Chang Wu, Kuo-Chen Hung, Gia-Shie Liu, Chih-Hung Jen, Intuitionistic fuzzy c-means clustering algorithm with neighborhood attraction in segmenting medical image, Soft Computing. February 2015, Volume 19, Issue 2, pp 459–470
  18. Krassimir T. Atanassov, 1986,”Intuitionistic Fuzzy Sets”, Fuzzy Sets and Systems, Vol.20, pp.87-96.
  19. Chinatsu Arima, Taizo Hanai and Masahiro Okamoto,2003,” Gene Expression Analysis Using Fuzzy K-Means Clustering”, Genome Informatics ,Vol.14(1),pp. 334-335.
  20. E.W. Forgy (1965). "Cluster analysis of multivariate data: efficiency versus interpretability of classifications". Biometrics. 21: 768–769. JSTOR 2528559.
  21. J. C. Dunn (1973): "A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters", Journal of Cybernetics 3: 32-57
  22. J. C. Bezdek (1981): "Pattern Recognition with Fuzzy Objective Function Algorithms", Plenum Press, New York  Tariq Rashid: “Clustering”


MANET, cluster, Intuitionistic fuzzy K-means, uncertainty, energy consumption, distance