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Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks

Unmanned Aerial Vehicle (UAV) deployment and placement are largely dependent upon the available energy, feasible scenario, and secure network. The feasible placement of UAV nodes to cover the cellular networks need optimal altitude. The under or over-estimation of nodes’ air timing leads to of resou...

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Autores principales: Majeed, Saqib, Sohail, Adnan, Qureshi, Kashif Naseer, Iqbal, Saleem, Javed, Ibrahim Tariq, Crespi, Noel, Nagmeldin, Wamda, Abdelmaboud, Abdelzahir
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9414567/
https://www.ncbi.nlm.nih.gov/pubmed/36015890
http://dx.doi.org/10.3390/s22166130
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author Majeed, Saqib
Sohail, Adnan
Qureshi, Kashif Naseer
Iqbal, Saleem
Javed, Ibrahim Tariq
Crespi, Noel
Nagmeldin, Wamda
Abdelmaboud, Abdelzahir
author_facet Majeed, Saqib
Sohail, Adnan
Qureshi, Kashif Naseer
Iqbal, Saleem
Javed, Ibrahim Tariq
Crespi, Noel
Nagmeldin, Wamda
Abdelmaboud, Abdelzahir
author_sort Majeed, Saqib
collection PubMed
description Unmanned Aerial Vehicle (UAV) deployment and placement are largely dependent upon the available energy, feasible scenario, and secure network. The feasible placement of UAV nodes to cover the cellular networks need optimal altitude. The under or over-estimation of nodes’ air timing leads to of resource waste or inefficiency of the mission. Multiple factors influence the estimation of air timing, but the majority of the literature concentrates only on flying time. Some other factors also degrade network performance, such as unauthorized access to UAV nodes. In this paper, the UAV coverage issue is considered, and a Coverage Area Decision Model for UAV-BS is proposed. The proposed solution is designed for cellular network coverage by using UAV nodes that are controlled and managed for reallocation, which will be able to change position per requirements. The proposed solution is evaluated and tested in simulation in terms of its performance. The proposed solution achieved better results in terms of placement in the network. The simulation results indicated high performance in terms of high packet delivery, less delay, less overhead, and better malicious node detection.
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spelling pubmed-94145672022-08-27 Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks Majeed, Saqib Sohail, Adnan Qureshi, Kashif Naseer Iqbal, Saleem Javed, Ibrahim Tariq Crespi, Noel Nagmeldin, Wamda Abdelmaboud, Abdelzahir Sensors (Basel) Article Unmanned Aerial Vehicle (UAV) deployment and placement are largely dependent upon the available energy, feasible scenario, and secure network. The feasible placement of UAV nodes to cover the cellular networks need optimal altitude. The under or over-estimation of nodes’ air timing leads to of resource waste or inefficiency of the mission. Multiple factors influence the estimation of air timing, but the majority of the literature concentrates only on flying time. Some other factors also degrade network performance, such as unauthorized access to UAV nodes. In this paper, the UAV coverage issue is considered, and a Coverage Area Decision Model for UAV-BS is proposed. The proposed solution is designed for cellular network coverage by using UAV nodes that are controlled and managed for reallocation, which will be able to change position per requirements. The proposed solution is evaluated and tested in simulation in terms of its performance. The proposed solution achieved better results in terms of placement in the network. The simulation results indicated high performance in terms of high packet delivery, less delay, less overhead, and better malicious node detection. MDPI 2022-08-16 /pmc/articles/PMC9414567/ /pubmed/36015890 http://dx.doi.org/10.3390/s22166130 Text en © 2022 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
Majeed, Saqib
Sohail, Adnan
Qureshi, Kashif Naseer
Iqbal, Saleem
Javed, Ibrahim Tariq
Crespi, Noel
Nagmeldin, Wamda
Abdelmaboud, Abdelzahir
Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
title Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
title_full Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
title_fullStr Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
title_full_unstemmed Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
title_short Coverage Area Decision Model by Using Unmanned Aerial Vehicles Base Stations for Ad Hoc Networks
title_sort coverage area decision model by using unmanned aerial vehicles base stations for ad hoc networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9414567/
https://www.ncbi.nlm.nih.gov/pubmed/36015890
http://dx.doi.org/10.3390/s22166130
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