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Minimal Increase Network Coding for Dynamic Networks

Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed....

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Detalles Bibliográficos
Autores principales: Zhang, Guoyin, Fan, Xu, Wu, Yanxia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4750993/
https://www.ncbi.nlm.nih.gov/pubmed/26867211
http://dx.doi.org/10.1371/journal.pone.0148725
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author Zhang, Guoyin
Fan, Xu
Wu, Yanxia
author_facet Zhang, Guoyin
Fan, Xu
Wu, Yanxia
author_sort Zhang, Guoyin
collection PubMed
description Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery.
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spelling pubmed-47509932016-02-26 Minimal Increase Network Coding for Dynamic Networks Zhang, Guoyin Fan, Xu Wu, Yanxia PLoS One Research Article Because of the mobility, computing power and changeable topology of dynamic networks, it is difficult for random linear network coding (RLNC) in static networks to satisfy the requirements of dynamic networks. To alleviate this problem, a minimal increase network coding (MINC) algorithm is proposed. By identifying the nonzero elements of an encoding vector, it selects blocks to be encoded on the basis of relationship between the nonzero elements that the controls changes in the degrees of the blocks; then, the encoding time is shortened in a dynamic network. The results of simulations show that, compared with existing encoding algorithms, the MINC algorithm provides reduced computational complexity of encoding and an increased probability of delivery. Public Library of Science 2016-02-11 /pmc/articles/PMC4750993/ /pubmed/26867211 http://dx.doi.org/10.1371/journal.pone.0148725 Text en © 2016 Zhang et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Zhang, Guoyin
Fan, Xu
Wu, Yanxia
Minimal Increase Network Coding for Dynamic Networks
title Minimal Increase Network Coding for Dynamic Networks
title_full Minimal Increase Network Coding for Dynamic Networks
title_fullStr Minimal Increase Network Coding for Dynamic Networks
title_full_unstemmed Minimal Increase Network Coding for Dynamic Networks
title_short Minimal Increase Network Coding for Dynamic Networks
title_sort minimal increase network coding for dynamic networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4750993/
https://www.ncbi.nlm.nih.gov/pubmed/26867211
http://dx.doi.org/10.1371/journal.pone.0148725
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