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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Weight convergence analysis of DV-hop localization algorithm with GA
Creator	Chen, • Cai, Xingjuan Cui, Zhihua Wang, Penghong Zhang, Wensheng
Source	Medline; PMC
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.
has issue date	2020-06-18(xsd:dateTime)
bibo:doi	10.1007/s00500-020-05088-z
bibo:pmid	32837291
has license	no-cc
sha1sum (hex)	93b763260242f5e0b1ae97f0de16e55bfe6eb529
schema:url	https://doi.org/10.1007/s00500-020-05088-z
resource representing a document's title	Weight convergence analysis of DV-hop localization algorithm with GA
has PubMed Central identifier	PMC7301770
has PubMed identifier	32837291
schema:publication	Soft comput
resource representing a document's body	covid:93b763260242f5e0b1ae97f0de16e55bfe6eb529#body_text
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