GDFEC Protocol for Heterogeneous Wireless Sensor Network

  • S. Swapna Kumar
  • S. Vishwas
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 31)


Wireless sensor networks (WSNs) in recent years shown abrupt growth in technological applications. The main research goals of WSN in the area of heterogeneity are to achieve various matrix performances such as high energy efficiency, lifetime and packet delivery nodes. Most proposed clustering algorithms do not consider the situation causes hot spot problems in multi-hop WSNs. To achieve such network the two soft computing techniques applied to energy efficient clustered heterogeneous sensor node network. In this paper proposed the implementation of the real time energy efficient clustering using a Genetic Dual Fuzzy Entropy Clustering (GDFEC) algorithm. Various matrixes of simulation carried out using MATLAB to study the performance under setup conditions. This creates a standardized power distribution among disseminated cluster nodes in the heterogeneous network. The protocol realization carried out on software simulation by different empirical test. The empirical analysis of GDFEC protocol compared with different traditional protocol to evaluate the level of resultant matrix. The protocol evaluation studies have shown that GDFEC protocol able to improve the network performance matrix under the heterogeneous distribution of network nodes.


Black hole Clustering Entropy-based algorithms Fuzzy clustering Genetic Wireless sensor networks 


  1. 1.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: Wireless sensor networks: a survey. Comput. Netw. 38, 393–422 (2002)CrossRefGoogle Scholar
  2. 2.
    Heinzelman, W.R., Chandrakasan, A., Balakrishnan, H.: Energy-efficient routing protocols for wireless microsensor networks. In: Proceeding 33rd Hawaii International Conference on System Sciences, vol. 8, pp. 8020–8030. (2000)Google Scholar
  3. 3.
    Smaragdakis, G., Matta, I., Bestavros, A.: A stable election protocol for clustered heterogeneous wireless sensor networks. Technical report, Boston University Computer Science Department. (2004)Google Scholar
  4. 4.
    Akkaya, K., Younis, M.: A survey on routing protocols for wireless sensor networks’. Ad Hoc Netw. 3, 325–349 (2005)CrossRefGoogle Scholar
  5. 5.
    Calvo, R., Figueiredo, M.: Reinforcement learning for hierarchical and modular network in autonomous robot navigation. In: Proceedings of the International Joint Conference on Neural Networks, vol. 1-4, pp. 1340–1345 (2003)Google Scholar
  6. 6.
    Ma, X., Liu, W., Li, Y., Song, R.: LVQ neural network based target differentiation method for mobile robot. In: Proceedings of the International Conference on Advanced Robotics, vol. 2005, pp. 680–685 (2005)Google Scholar
  7. 7.
    Li, T.S., Chang, S.J., Tong, W.: Fuzzy target tracking control of autonomous mobile robots by using infrared sensors. IEEE Trans. Fuzzy Syst. 12(4), 491–501 (2004)CrossRefGoogle Scholar
  8. 8.
    Ming, L., Zailin, G., Shuzi, Y.: Mobile robot fuzzy control optimization using genetic algorithm. Artif. Intell. Eng. 10(4), 293–298 (1996)CrossRefGoogle Scholar
  9. 9.
    Moreno, L., Armingol, J.M., Garrido, S., Escalera, A.D.L., Salishs, M.A.: Genetic algorithm for mobile robot localization using ultrasonic sensors. J. Intell. Rob. Syst. 34(2), 135–154 (2002)CrossRefMATHGoogle Scholar
  10. 10.
    Heinzelman, W., Chandrakasan, A., Balakrishnan, H.: Energy-efficient communication protocol for wireless micro-sensor networks. In: Proceedings of the 33rd Hawaii International Conference on System Sciences (HICSS ‘00) (2000)Google Scholar
  11. 11.
    Fu, L., Medico, E.: FLAME: a novel fuzzy clustering method for the analysis of dna microarray data. BMC Bioinform. 8, 3 (2007). doi: 10.1186/1471-2105-8-3 CrossRefGoogle Scholar
  12. 12.
    Yao, J., Dash, M., Tan, S.T., Liu, H.: Entropy-based fuzzy clustering and fuzzy modeling. Fuzzy Sets Syst. 113, 381–388 (2000)CrossRefMATHGoogle Scholar
  13. 13.
    Aderohunmu, F.A., Deng, J.D., Wu, X.H., Wang, S.: Performance comparison of LEACH and LEACH-C protocols by NS2. In: Proceedings of 9th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, pp. 254–258. Hong Kong (2010)Google Scholar
  14. 14.
    Bala, M., Awasthi, L.: On proficiency of HEED protocol with heterogeneity for wireless sensor networks with BS and nodes mobility. Int. J. Intell. Syst. Appl. 4(7), 58 (2012)Google Scholar
  15. 15.
    Liu, T., Peng, J., Yang, J., Wang, C.: Energy efficient prediction clustering algorithm for multilevel heterogeneous wireless sensor networks. Cite as: arXiv: 1105.6237 [cs.NI] (2011)
  16. 16.
    Brahim, E., Rachid, S., Zamora, A.P., Aboutajdine, D.: Stochastic and balanced distributed energy-efficient clustering (SBDEEC) for heterogeneous wireless sensor networks. INFOCOMP J. Comput. Sci. 8(3), 11–20 (2009)Google Scholar
  17. 17.
    Zadeh, L.A.: Fuzzy logic, neural networks, and soft computing. Commun. ACM 37, 77–84 (1994)CrossRefGoogle Scholar
  18. 18.
    Kumar, S.S., Kumar, M.N., Sheeba, V.S., Kashwan, K.R.: Cluster based routing algorithm using dual staged fuzzy logic in wireless sensor networks. J. Inform. Comput. Sci. 9(5), 1281–1297. ISSN: 1548–7741 (2012)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringVidya Academy of Science and TechnologyThrissurIndia
  2. 2.Department of Computer Science and EngineeringKVG College of EngineeringSulliaIndia

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