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Adaptive Discrete Vector Field in Sensor Networks
Homology groups are a prime tool for measuring the connectivity of a network, and their computation in a distributed and adaptive way is mandatory for their use in sensor networks. In this paper, we propose a solution based on the construction of an adaptive discrete vector field from where, thanks...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111330/ https://www.ncbi.nlm.nih.gov/pubmed/30103546 http://dx.doi.org/10.3390/s18082642 |
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author | Zhang, Mengyi Goupil, Alban |
author_facet | Zhang, Mengyi Goupil, Alban |
author_sort | Zhang, Mengyi |
collection | PubMed |
description | Homology groups are a prime tool for measuring the connectivity of a network, and their computation in a distributed and adaptive way is mandatory for their use in sensor networks. In this paper, we propose a solution based on the construction of an adaptive discrete vector field from where, thanks to the discrete Morse theory, the generators of the homology groups are extracted. The efficiency and the adaptability of our approach are tested against two applications: the detection and the localization of the holes in the coverage, and the selection of active sensors ensuring complete coverage. |
format | Online Article Text |
id | pubmed-6111330 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-61113302018-08-30 Adaptive Discrete Vector Field in Sensor Networks Zhang, Mengyi Goupil, Alban Sensors (Basel) Article Homology groups are a prime tool for measuring the connectivity of a network, and their computation in a distributed and adaptive way is mandatory for their use in sensor networks. In this paper, we propose a solution based on the construction of an adaptive discrete vector field from where, thanks to the discrete Morse theory, the generators of the homology groups are extracted. The efficiency and the adaptability of our approach are tested against two applications: the detection and the localization of the holes in the coverage, and the selection of active sensors ensuring complete coverage. MDPI 2018-08-12 /pmc/articles/PMC6111330/ /pubmed/30103546 http://dx.doi.org/10.3390/s18082642 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhang, Mengyi Goupil, Alban Adaptive Discrete Vector Field in Sensor Networks |
title | Adaptive Discrete Vector Field in Sensor Networks |
title_full | Adaptive Discrete Vector Field in Sensor Networks |
title_fullStr | Adaptive Discrete Vector Field in Sensor Networks |
title_full_unstemmed | Adaptive Discrete Vector Field in Sensor Networks |
title_short | Adaptive Discrete Vector Field in Sensor Networks |
title_sort | adaptive discrete vector field in sensor networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6111330/ https://www.ncbi.nlm.nih.gov/pubmed/30103546 http://dx.doi.org/10.3390/s18082642 |
work_keys_str_mv | AT zhangmengyi adaptivediscretevectorfieldinsensornetworks AT goupilalban adaptivediscretevectorfieldinsensornetworks |