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Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety

Real-time data- and location-sharing using mesh networking radios paired with smartphones may improve situational awareness and safety in remote environments lacking communications infrastructure. Despite being increasingly used for wildland fire and public safety applications, there has been little...

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Autores principales: Zimbelman, Eloise G., Keefe, Robert F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728932/
https://www.ncbi.nlm.nih.gov/pubmed/36477301
http://dx.doi.org/10.1371/journal.pone.0278645
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author Zimbelman, Eloise G.
Keefe, Robert F.
author_facet Zimbelman, Eloise G.
Keefe, Robert F.
author_sort Zimbelman, Eloise G.
collection PubMed
description Real-time data- and location-sharing using mesh networking radios paired with smartphones may improve situational awareness and safety in remote environments lacking communications infrastructure. Despite being increasingly used for wildland fire and public safety applications, there has been little formal evaluation of the network connectivity of these devices. The objectives of this study were to 1) characterize the connectivity of mesh networks in variable forest and topographic conditions; 2) evaluate the abilities of lidar and satellite remote sensing data to predict connectivity; and 3) assess the relative importance of the predictive metrics. A large field experiment was conducted to test the connectivity of a network of one mobile and five stationary goTenna Pro mesh radios on 24 Public Land Survey System sections approximately 260 ha in area in northern Idaho. Dirichlet regression was used to predict connectivity using 1) both lidar- and satellite-derived metrics (LIDSAT); 2) lidar-derived metrics only (LID); and 3) satellite-derived metrics only (SAT). On average the full network was connected only 32.6% of the time (range: 0% to 90.5%) and the mobile goTenna was disconnected from all other devices 18.2% of the time (range: 0% to 44.5%). RMSE for the six connectivity levels ranged from 0.101 to 0.314 for the LIDSAT model, from 0.103 to 0.310 for the LID model, and from 0.121 to 0.313 for the SAT model. Vegetation-related metrics affected connectivity more than topography. Developed models may be used to predict the connectivity of real-time mesh networks over large spatial extents using remote sensing data in order to forecast how well similar networks are expected to perform for wildland firefighting, forestry, and public safety applications. However, safety professionals should be aware of the impacts of vegetation on connectivity.
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spelling pubmed-97289322022-12-08 Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety Zimbelman, Eloise G. Keefe, Robert F. PLoS One Research Article Real-time data- and location-sharing using mesh networking radios paired with smartphones may improve situational awareness and safety in remote environments lacking communications infrastructure. Despite being increasingly used for wildland fire and public safety applications, there has been little formal evaluation of the network connectivity of these devices. The objectives of this study were to 1) characterize the connectivity of mesh networks in variable forest and topographic conditions; 2) evaluate the abilities of lidar and satellite remote sensing data to predict connectivity; and 3) assess the relative importance of the predictive metrics. A large field experiment was conducted to test the connectivity of a network of one mobile and five stationary goTenna Pro mesh radios on 24 Public Land Survey System sections approximately 260 ha in area in northern Idaho. Dirichlet regression was used to predict connectivity using 1) both lidar- and satellite-derived metrics (LIDSAT); 2) lidar-derived metrics only (LID); and 3) satellite-derived metrics only (SAT). On average the full network was connected only 32.6% of the time (range: 0% to 90.5%) and the mobile goTenna was disconnected from all other devices 18.2% of the time (range: 0% to 44.5%). RMSE for the six connectivity levels ranged from 0.101 to 0.314 for the LIDSAT model, from 0.103 to 0.310 for the LID model, and from 0.121 to 0.313 for the SAT model. Vegetation-related metrics affected connectivity more than topography. Developed models may be used to predict the connectivity of real-time mesh networks over large spatial extents using remote sensing data in order to forecast how well similar networks are expected to perform for wildland firefighting, forestry, and public safety applications. However, safety professionals should be aware of the impacts of vegetation on connectivity. Public Library of Science 2022-12-07 /pmc/articles/PMC9728932/ /pubmed/36477301 http://dx.doi.org/10.1371/journal.pone.0278645 Text en © 2022 Zimbelman, Keefe https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://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
Zimbelman, Eloise G.
Keefe, Robert F.
Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety
title Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety
title_full Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety
title_fullStr Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety
title_full_unstemmed Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety
title_short Lost in the woods: Forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety
title_sort lost in the woods: forest vegetation, and not topography, most affects the connectivity of mesh radio networks for public safety
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9728932/
https://www.ncbi.nlm.nih.gov/pubmed/36477301
http://dx.doi.org/10.1371/journal.pone.0278645
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