Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Zhang, Mengyi, Goupil, Alban
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
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
_version_ 1783350635944476672
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