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Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring

This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable algorithm to estimate the minimum number of drones and...

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Detalles Bibliográficos
Autores principales: Savkin, Andrey V., Huang, Hailong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539925/
https://www.ncbi.nlm.nih.gov/pubmed/31058833
http://dx.doi.org/10.3390/s19092068
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author Savkin, Andrey V.
Huang, Hailong
author_facet Savkin, Andrey V.
Huang, Hailong
author_sort Savkin, Andrey V.
collection PubMed
description This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable algorithm to estimate the minimum number of drones and determine their locations is developed. Moreover, it is proved that this algorithm is asymptotically optimal in the sense that the ratio of the number of drones required by this algorithm and the minimum number of drones converges to one as the area of the ground region tends to infinity. The proof is based on Kershner’s theorem from combinatorial geometry. Illustrative examples and comparisons with other existing methods show the efficiency of the developed algorithm.
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spelling pubmed-65399252019-06-04 Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring Savkin, Andrey V. Huang, Hailong Sensors (Basel) Article This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable algorithm to estimate the minimum number of drones and determine their locations is developed. Moreover, it is proved that this algorithm is asymptotically optimal in the sense that the ratio of the number of drones required by this algorithm and the minimum number of drones converges to one as the area of the ground region tends to infinity. The proof is based on Kershner’s theorem from combinatorial geometry. Illustrative examples and comparisons with other existing methods show the efficiency of the developed algorithm. MDPI 2019-05-03 /pmc/articles/PMC6539925/ /pubmed/31058833 http://dx.doi.org/10.3390/s19092068 Text en © 2019 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
Savkin, Andrey V.
Huang, Hailong
Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
title Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
title_full Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
title_fullStr Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
title_full_unstemmed Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
title_short Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
title_sort asymptotically optimal deployment of drones for surveillance and monitoring
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539925/
https://www.ncbi.nlm.nih.gov/pubmed/31058833
http://dx.doi.org/10.3390/s19092068
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