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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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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. |
format | Online Article Text |
id | pubmed-6539925 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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