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Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet

Unmanned Aerial Vehicles (UAVs, also known as drones) have become increasingly appealing with various applications and services over the past years. Drone-based remote sensing has shown its unique advantages in collecting ground-truth and real-time data due to their affordable costs and relative eas...

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Detalles Bibliográficos
Autores principales: Luo, Yawen, Chen, Yuhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068405/
https://www.ncbi.nlm.nih.gov/pubmed/33918003
http://dx.doi.org/10.3390/s21082622
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author Luo, Yawen
Chen, Yuhua
author_facet Luo, Yawen
Chen, Yuhua
author_sort Luo, Yawen
collection PubMed
description Unmanned Aerial Vehicles (UAVs, also known as drones) have become increasingly appealing with various applications and services over the past years. Drone-based remote sensing has shown its unique advantages in collecting ground-truth and real-time data due to their affordable costs and relative ease of operability. This paper presents a 3D placement scheme for multi-drone sensing/monitoring platforms, where a fleet of drones are sent for conducting a mission in a given area. It can range from environmental monitoring of forestry, survivors searching in a disaster zone to exploring remote regions such as deserts and mountains. The proposed drone placing algorithm covers the entire region without dead zones while minimizing the number of cooperating drones deployed. Naturally, drones have limited battery supplies which need to cover mechanical motions, message transmissions and data calculation. Consequently, the drone energy model is explicitly investigated and dynamic adjustments are deployed on drone locations. The proposed drone placement algorithm is 3D landscaping-aware and it takes the line-of-sight into account. The energy model considers inter-communications within drones. The algorithm not only minimizes the overall energy consumption, but also maximizes the whole drone team’s lifetime in situations where no power recharging facilities are available in remote/rural areas. Simulations show the proposed placement scheme has significantly prolonged the lifetime of the drone fleet with the least number of drones deployed under various complex terrains.
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spelling pubmed-80684052021-04-25 Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet Luo, Yawen Chen, Yuhua Sensors (Basel) Article Unmanned Aerial Vehicles (UAVs, also known as drones) have become increasingly appealing with various applications and services over the past years. Drone-based remote sensing has shown its unique advantages in collecting ground-truth and real-time data due to their affordable costs and relative ease of operability. This paper presents a 3D placement scheme for multi-drone sensing/monitoring platforms, where a fleet of drones are sent for conducting a mission in a given area. It can range from environmental monitoring of forestry, survivors searching in a disaster zone to exploring remote regions such as deserts and mountains. The proposed drone placing algorithm covers the entire region without dead zones while minimizing the number of cooperating drones deployed. Naturally, drones have limited battery supplies which need to cover mechanical motions, message transmissions and data calculation. Consequently, the drone energy model is explicitly investigated and dynamic adjustments are deployed on drone locations. The proposed drone placement algorithm is 3D landscaping-aware and it takes the line-of-sight into account. The energy model considers inter-communications within drones. The algorithm not only minimizes the overall energy consumption, but also maximizes the whole drone team’s lifetime in situations where no power recharging facilities are available in remote/rural areas. Simulations show the proposed placement scheme has significantly prolonged the lifetime of the drone fleet with the least number of drones deployed under various complex terrains. MDPI 2021-04-08 /pmc/articles/PMC8068405/ /pubmed/33918003 http://dx.doi.org/10.3390/s21082622 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Luo, Yawen
Chen, Yuhua
Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet
title Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet
title_full Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet
title_fullStr Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet
title_full_unstemmed Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet
title_short Energy-Aware Dynamic 3D Placement of Multi-Drone Sensing Fleet
title_sort energy-aware dynamic 3d placement of multi-drone sensing fleet
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8068405/
https://www.ncbi.nlm.nih.gov/pubmed/33918003
http://dx.doi.org/10.3390/s21082622
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