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A Virtual Force Algorithm-Lévy-Embedded Grey Wolf Optimization Algorithm for Wireless Sensor Network Coverage

Shipeng Wang1, Xiaoping Yang2, Xingqiao Wang3

  • 1College of Communication Engineering, Jilin University, Changchun 130012, China. spwang17@mails.jlu.edu.cn.

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|June 21, 2019
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Summary
This summary is machine-generated.

A new Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm improves wireless sensor network coverage. This method enhances coverage rate, uniformity, and node movement efficiency compared to other algorithms.

Keywords:
Lévy-embedded Grey Wolf Optimization algorithmVirtual Force algorithmcoverage optimizationwireless sensor network

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Random deployment of large-scale wireless sensor networks (WSNs) in outdoor environments leads to suboptimal coverage.
  • Achieving efficient and uniform coverage is crucial for effective WSN monitoring applications.

Purpose of the Study:

  • To propose a novel coverage optimization algorithm for WSNs.
  • To enhance the coverage rate, uniformity, and energy efficiency of sensor nodes.

Main Methods:

  • Development of the Virtual Force-Lévy-embedded Grey Wolf Optimization (VFLGWO) algorithm.
  • Comparative analysis against Lévy-embedded Grey Wolf Optimizer, Cuckoo Search, and Chaotic Particle Swarm Optimization algorithms.
  • Simulation-based evaluation of algorithm performance.

Main Results:

  • The VFLGWO algorithm demonstrated superior performance in improving coverage rate and uniformity.
  • VFLGWO achieved a reduced average moving distance for sensor nodes compared to benchmark algorithms.
  • The algorithm exhibited robust adaptability to variations in node numbers and monitoring area size.

Conclusions:

  • The VFLGWO algorithm offers a significant improvement for WSN coverage optimization.
  • This approach provides a more efficient and adaptable solution for deploying sensor networks in diverse environments.