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Weight convergence analysis of DV-hop localization algorithm with GA.

Xingjuan Cai1, Penghong Wang1, Zhihua Cui1

  • 1School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024 China.

Soft Computing
|August 25, 2020
PubMed
Summary

This study introduces a mathematical model for the distance vector-hop (DV-hop) localization algorithm, enhancing positional precision by analyzing weights and hop counts. The developed model converges to a positioning error of 1/4R.

Keywords:
Convergence analysesDV-hopGenetic algorithm (GA)Mathematical weight modelPositional precision

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Area of Science:

  • Wireless Sensor Networks
  • Localization Algorithms
  • Network Security

Background:

  • The distance vector-hop (DV-hop) algorithm is a common method for localizing sensor nodes based on hop counts.
  • Current methods lack a clear mathematical model linking hop count, weight, and positioning error.
  • Enhancing positional precision in DV-hop requires a better understanding of these relationships.

Purpose of the Study:

  • To develop a mathematical model for the relationship between weights and hop counts in the DV-hop algorithm.
  • To analyze the convergence properties of this new mathematical model.
  • To improve the accuracy of sensor node localization using a weighted DV-hop approach.

Main Methods:

  • Constructed a novel mathematical model connecting weights and hop counts.
  • Analyzed the convergence of the proposed mathematical model.
  • Employed a genetic algorithm to solve the mathematical weighted DV-hop (MW-GADV-hop) positioning model.

Main Results:

  • The developed mathematical model demonstrates logical construction.
  • Simulation results show the positioning error of the MW-GADV-hop model converges to 1/4R.
  • The proposed weighting strategy enhances positional precision in DV-hop localization.

Conclusions:

  • The established mathematical model provides a theoretical foundation for weighted DV-hop localization.
  • The genetic algorithm effectively solves the MW-GADV-hop model, achieving improved accuracy.
  • This research offers a pathway to more precise sensor node localization in wireless networks.