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A Novel Coverage Optimization Strategy Based on Grey Wolf Algorithm Optimized by Simulated Annealing for Wireless

Yong Zhang1, Li Cao2,3, Yinggao Yue1,3

  • 1School of Mathematics and Computer Science, Hubei University of Arts and Science, Xiangyang 441053, China.

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This study introduces a new grey wolf algorithm enhanced with simulated annealing to optimize wireless sensor network coverage. This method improves network coverage, reduces energy use, and extends network life compared to existing algorithms.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Optimization

Background:

  • Wireless sensor networks (WSNs) face coverage optimization challenges, leading to node redundancy and reduced network lifespan.
  • Random deployment of sensor nodes often results in high aggregation and low coverage rates.
  • Improving WSN coverage is crucial for network stability, energy efficiency, and longevity.

Purpose of the Study:

  • To address the coverage optimization problem in wireless sensor networks.
  • To propose a novel algorithm that enhances node distribution and network coverage.
  • To improve the efficiency and lifespan of wireless sensor networks.

Main Methods:

  • Established a mathematical model for wireless sensor network coverage optimization.
  • Developed a novel grey wolf algorithm integrated with simulated annealing.
  • Embedded simulated annealing within the grey wolf algorithm's update process to boost global optimization and convergence.

Main Results:

  • The enhanced grey wolf algorithm significantly improved network coverage.
  • The proposed method demonstrated reduced energy consumption among sensor nodes.
  • Simulation experiments confirmed faster optimization speeds compared to standard grey wolf and particle swarm optimization algorithms.
  • The algorithm successfully prolonged the overall network life cycle.

Conclusions:

  • The simulated annealing-optimized grey wolf algorithm is effective for WSN coverage optimization.
  • This approach offers superior performance in terms of coverage, speed, and energy efficiency.
  • The findings contribute to more stable and longer-lasting wireless sensor networks.