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

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 small...

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Vydané v:Soft computing (Berlin, Germany) Ročník 24; číslo 23; s. 18249 - 18258
Hlavní autori: Cai, Xingjuan, Wang, Penghong, Cui, Zhihua, Zhang, Wensheng, Chen, Jinjun
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2020
Springer Nature B.V
Predmet:
Artificial Intelligence
Computational Intelligence
Control
Convergence
Engineering
Genetic algorithms
Localization
Mathematical analysis
Mathematical Logic and Foundations
Mathematical models
Mechatronics
Methodologies and Application
Nodes
Optimization algorithms
Robotics
Sensors
Wireless communications
Genetic algorithm (GA)
Positional precision
Convergence analyses
Mathematical weight model
DV-hop
ISSN:1432-7643, 1433-7479
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Shrnutí: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 1/4 R .
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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Communicated by V. Loia.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-020-05088-z