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Enhancing the Sensor Node Localization Algorithm Based on Improved DV-Hop and DE Algorithms in Wireless Sensor

Dezhi Han1, Yunping Yu1, Kuan-Ching Li2

  • 1College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China.

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|January 16, 2020
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Summary
This summary is machine-generated.

This study introduces DEIDV-Hop, an improved localization algorithm for wireless sensor networks. It enhances accuracy and stability by combining differential evolution and particle swarm optimization, outperforming existing methods.

Keywords:
DV-Hopdifferential evolutionnode localizationwireless sensor networks

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Area of Science:

  • Computer Science
  • Wireless Sensor Networks
  • Localization Algorithms

Background:

  • The Distance Vector-Hop (DV-Hop) algorithm is a common range-free localization method in wireless sensor networks.
  • DV-Hop suffers from limited localization accuracy, particularly concerning the average distance per hop.
  • Existing algorithms require improvement to meet demands for precise node positioning.

Purpose of the Study:

  • To propose DEIDV-Hop, an enhanced localization algorithm for wireless sensor networks.
  • To improve the accuracy and stability of node localization compared to traditional DV-Hop.
  • To address the limitations of DV-Hop, including potential errors in average hop distance calculation.

Main Methods:

  • The proposed DEIDV-Hop algorithm integrates differential evolution (DE) and particle swarm optimization (PSO).
  • Random mutation is introduced into the DE algorithm to enhance population diversity and overcome premature convergence.
  • PSO's social learning is embedded in the crossover operation to accelerate convergence and improve optimization results.

Main Results:

  • DEIDV-Hop demonstrated smaller localization errors across four different network environments.
  • The proposed algorithm exhibited superior stability compared to existing localization methods.
  • Simulation results validate the effectiveness of the enhanced DE algorithm in finding optimal solutions for node location.

Conclusions:

  • DEIDV-Hop significantly improves upon the DV-Hop algorithm's localization accuracy and stability.
  • The integration of DE and PSO offers a robust approach for wireless sensor network localization.
  • The algorithm shows promise for applications demanding high localization precision and reliability.