A new model to determine the hierarchical structure of the wireless sensor networks

A new model to determine the hierarchical structure of the wireless sensor networks

Wireless sensor networks are one of the rising areas of scientific research. Common purpose of theseinvestigations is usually constructing optimal structure of the network by prolonging its lifetime. In this study, a newmodel has been proposed to construct a hierarchical structure of wireless sensor networks. Methods used in the model todetermine clusters and appropriate cluster heads are k-means clustering and fuzzy inference system (FIS), respectively.The weighted averaging based on levels (WABL) defuzzification method is used to calculate crisp outputs of the FIS. Anew theorem for calculation of WABL values has been proved in order to simplify getting the crisp values from complexfuzzy outputs of the FIS. The proposed methodology is experimented via simulation example, and experiments confirmits validity.

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  • [1] Gupta I, Riordan D, Sampalli S. Cluster-head election using fuzzy logic for wireless sensor networks. In: 3rd Annual Communication Networks and Services Research Conference; Halifax, NS, Canada; 2005. pp. 255-260.
  • [2] Sharma T, Kumar B. F-MCHEL: Fuzzy based master cluster head election leach protocol in wireless sensor network. International Journal of Computer Science and Telecommunications 2012; 3(10): 8-13.
  • [3] Alami H, Najid A. SEFP: A new routing approach using fuzzy logic for clustered heterogeneous wireless sensor networks. International Journal on Smart Sensing and Intelligent Systems 2015; 8(4): 2286-2306.
  • [4] Bidaki M, Tabbakh RK. Efficient fuzzy logic-based clustering algorithm for wireless sensor networks. International Journal of Grid and Distributed Computing 2016; 9(5): 79-88.
  • [5] Sert SA, Yazici A, Dokeroglu T. Fuzzy processing in surveillance wireless sensor networks. In: IEEE International Conference on Fuzzy Systems; Vancouver, BC, Canada; 2016. pp. 1509-1515.
  • [6] Alami H, Najid A. Fuzzy logic based clustering algorithm for wireless sensor networks. International Journal of Fuzzy System Applications 2017; 6(4): 63-82.
  • [7] Balakrishnan B, Balachandran S. FLECH: fuzzy logic based energy efficient clustering hierarchy for nonuniform wireless sensor networks. Wireless Communications and Mobile Computing 2017; 3: 1-13.
  • [8] Zhang Y, Wang J, Han D, Wu H, Zhou R. Fuzzy-logic based distributed energy-efficient clustering algorithm for wireless sensor networks. Sensors 2017; 17: 1-21. article no. 1554.
  • [9] Nehra V, Pal R, Sharma AK. Fuzzy-based leader selection for topology controlled PEGASIS protocol for lifetime enhancement in wireless sensor network. International Journal of Computers and Technology 2013; 4(3): 755-764.
  • [10] Sharma D, Verma S, Sharma K. Network topologies in wireless sensor networks: a review. International Journal of Electronics and Communication Technology 2013; 4(3): 93-97.
  • [11] Wang J, Yang X, Zheng Y, Zhang J, Kim JU. An energy-efficient multi-hop hierarchical routing protocol for wireless sensor networks. International Journal of Future Generation Communication and Networking 2012; 5(4): 89-98.
  • [12] Iancu I. A Mamdani type fuzzy logic controller. In: Dadios E (editor). Fuzzy Logic - Controls, Concepts, Theories and Applications. InTechOpen, 2012, pp.325-350.
  • [13] Jain N, Gupta SK, Sinha P. Clustering protocols in wireless sensor networks: a survey. International Journal of Applied Information Systems 2013; 5(2): 41-50.
  • [14] Muhammed A. Kablosuz algılayıcı ağlarda kümeleme algoritmaları ile enerji verimliliğinin arttırılması için alternatif bir yöntem geliştirme. Doktora Tezi, Gazi Üniversitesi, Ankara, Türkiye, 2016 (in Turkish).
  • [15] Najmeh KP. Energy efficiency in wireless sensor networks. PhD, University of Technology, Sydney, Australia, 2015.
  • [16] Nasiboglu R, Abdullayeva R. Analytical formulations for the level based weıghted average value of dıscrete trapezoidal fuzzy numbers. International Journal on Soft Computing 2018; 9(2): 1-15.
  • [17] Nasibov EN, Mert A. On methods of defuzzification of parametrically represented fuzzy numbers. Automatic Control and Computer Sciences 2007; 41(5): 265-273.
  • [18] Nasibov E. Aggregation of fuzzy values in linear programming problems. Automatic Control and Computer Sciences 2003; 37(2): 1-11.