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Intelligent unequal clustering in wireless sensor networks using a game theoretic and evolutionary strategy.

Yanhui Qu1, Yingyi Qu2, Zhiqiang Zhu3

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Summary

This study introduces an intelligent unequal clustering method for Wireless Sensor Networks (WSNs) that balances energy consumption. The optimized approach significantly extends network operational time by improving energy efficiency and reliability.

Keywords:
Coyote optimization algorithmFuzzy logicGame theoryUnequal clusteringWireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Traditional clustering in Wireless Sensor Networks (WSNs) leads to uneven energy distribution and premature network failure.
  • Non-uniform cluster head distribution fails to adapt to varying node density and traffic, causing rapid energy depletion in some nodes while others remain idle.

Purpose of the Study:

  • To develop an intelligent unequal clustering technique for WSNs to enhance energy efficiency and network lifetime.
  • To address energy imbalances and improve load distribution in WSNs.

Main Methods:

  • Utilized the Coyote Optimization Algorithm (COA) for adaptive cluster head selection.
  • Implemented fuzzy logic to dynamically adjust cluster radii based on network conditions.
  • Employed a game-theoretic approach for energy-efficient data routing.

Main Results:

  • The proposed method significantly outperformed traditional EE-LEACH, extending network lifetime by 124.5%.
  • Demonstrated substantial improvements in energy efficiency, network reliability, and overall operational duration.
  • Showcased effective load balancing across diverse network conditions.

Conclusions:

  • The intelligent unequal clustering method offers a robust solution for improving WSN performance.
  • Dynamic clustering combined with optimized routing enhances energy efficiency, reliability, and network longevity.