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Analytical network process based optimum cluster head selection in wireless sensor network.

Haleem Farman1, Huma Javed1, Bilal Jan2,3

  • 1Department of Computer Science, University of Peshawar, Peshawar, Pakistan.

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|July 19, 2017
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Summary

This study applies the Analytical Network Process (ANP) model for optimal cluster head selection in Wireless Sensor Networks (WSNs). The ANP model enhances network lifetime by efficiently managing resources and minimizing cluster head reselection.

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Area of Science:

  • Computer Science
  • Network Engineering
  • Artificial Intelligence

Background:

  • Wireless Sensor Networks (WSNs) face challenges with limited energy, computation, and memory in small sensor nodes.
  • Efficient resource management and network lifetime are critical for ubiquitous WSN applications.
  • Clustering nodes improves network management and overall efficiency.

Purpose of the Study:

  • To evaluate the applicability of the Analytical Network Process (ANP) model for cluster head (CH) selection in WSNs.
  • To enhance energy efficiency and extend the network lifetime of WSNs.
  • To investigate the ANP model's effectiveness in multi-criteria decision making for CH selection.

Main Methods:

  • Utilized a grid-based hybrid network deployment approach with a merge and split technique for topology construction.
  • Applied the Analytical Network Process (ANP) model for multi-criteria decision making in CH selection.
  • Considered five parameters: distance from nodes, residual energy level, distance from centroid, times node selected as CH, and merged nodes.

Main Results:

  • The ANP model demonstrated effectiveness in selecting optimal cluster heads based on multiple criteria.
  • Sensitivity analysis confirmed the stability of node rankings under different scenarios.
  • The proposed ANP-based method outperformed existing energy-efficient clustering protocols.

Conclusions:

  • The Analytical Network Process (ANP) is a viable and effective model for cluster head selection in Wireless Sensor Networks.
  • The ANP model contributes to extending WSN network lifetime through optimized resource management and reduced CH reselection.
  • This research provides a better understanding of component dependencies in WSN evaluation processes using ANP.