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

Authors
  • Cai, Xingjuan1
  • Wang, Penghong1
  • Cui, Zhihua1
  • Zhang, Wensheng2
  • Chen, Jinjun1, 3
  • 1 School of Computer Science and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024 China. , (China)
  • 2 State Key Laboratory of Intelligent Control and Management of Complex System, Institute of Automation, Chinese Academy of Sciences, Beijing, China. , (China)
  • 3 Department of Computer Science and Software Engineering, Swinburne University of Technology, Melbourne, 3000 Australia. , (Australia)
Type
Published Article
Journal
Soft computing
Publication Date
Jun 18, 2020
Pages
1–10
Identifiers
DOI: 10.1007/s00500-020-05088-z
PMID: 32837291
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

The distance vector-hop (DV-hop) is a typical localization algorithm. It estimates sensor nodes location through detecting the hop count between nodes. To enhance the positional precision, the weight is used to estimate position, and the conventional wisdom is that the more hop counts are, the smaller value of weight will be. However, there has been no clear mathematical model among positioning error, hop count, and weight. This paper constructs a mathematical model between the weights and hops and analyzes the convergence of this model. Finally, the genetic algorithm is used to solve this mathematical weighted DV-hop (MW-GADV-hop) positioning model, the simulation results illustrate that the model construction is logical, and the positioning error of the model converges to 1/4R. © Springer-Verlag GmbH Germany, part of Springer Nature 2020.

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