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A Low Complexity Persistent Reconnaissance Algorithm for FANET

In recent years, with the rapid progress of unmanned aerial vehicle (UAV) technology, UAV-based systems have been widely used in both civilian and military applications. Researchers have proposed various network architectures and routing protocols to address the network connectivity problems associa...

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Autores principales: Guo, Yuan, Tang, Hongying, Qin, Ronghua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737455/
https://www.ncbi.nlm.nih.gov/pubmed/36502226
http://dx.doi.org/10.3390/s22239526
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author Guo, Yuan
Tang, Hongying
Qin, Ronghua
author_facet Guo, Yuan
Tang, Hongying
Qin, Ronghua
author_sort Guo, Yuan
collection PubMed
description In recent years, with the rapid progress of unmanned aerial vehicle (UAV) technology, UAV-based systems have been widely used in both civilian and military applications. Researchers have proposed various network architectures and routing protocols to address the network connectivity problems associated with the high mobility of UAVs, and have achieved considerable results in a flying ad hoc network (FANET). Although scholars have noted various threats to UAVs in practical applications, such as local magnetic field variation, acoustic interference, and radio signal hijacking, few studies have taken into account the dynamic nature of these threat factors. Moreover, the UAVs’ high mobility combined with dynamic threats makes it more challenging to ensure connectivity while adapting to ever-changing scenarios. In this context, this paper introduces the concept of threat probability density function (threat PDF) and proposes a particle swarm optimization (PSO)-based threat avoidance and reconnaissance FANET construction algorithm (TARFC), which enables UAVs to dynamically adapt to avoid high-risk areas while maintaining FANET connectivity. Inspired by the graph editing distance, the total edit distance (TED) is defined to describe the alterations of the FANET and threat factors over time. Based on TED, a dynamic threat avoidance and continuous reconnaissance FANET operation algorithm (TA&CRFO) is proposed to realize semi-distributed control of the network. Simulation results show that both TARFC and TA&CRFO are effective in maintaining network connectivity and avoiding threats in dynamic scenarios. The average threat value of UAVs using TARFC and TA&CRFO is reduced by 3.99~27.51% and 3.07~26.63%, respectively, compared with the PSO algorithm. In addition, with limited distributed moderation, the complexity of the TA&CRFO algorithm is only 20.08% of that of TARFC.
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spelling pubmed-97374552022-12-11 A Low Complexity Persistent Reconnaissance Algorithm for FANET Guo, Yuan Tang, Hongying Qin, Ronghua Sensors (Basel) Article In recent years, with the rapid progress of unmanned aerial vehicle (UAV) technology, UAV-based systems have been widely used in both civilian and military applications. Researchers have proposed various network architectures and routing protocols to address the network connectivity problems associated with the high mobility of UAVs, and have achieved considerable results in a flying ad hoc network (FANET). Although scholars have noted various threats to UAVs in practical applications, such as local magnetic field variation, acoustic interference, and radio signal hijacking, few studies have taken into account the dynamic nature of these threat factors. Moreover, the UAVs’ high mobility combined with dynamic threats makes it more challenging to ensure connectivity while adapting to ever-changing scenarios. In this context, this paper introduces the concept of threat probability density function (threat PDF) and proposes a particle swarm optimization (PSO)-based threat avoidance and reconnaissance FANET construction algorithm (TARFC), which enables UAVs to dynamically adapt to avoid high-risk areas while maintaining FANET connectivity. Inspired by the graph editing distance, the total edit distance (TED) is defined to describe the alterations of the FANET and threat factors over time. Based on TED, a dynamic threat avoidance and continuous reconnaissance FANET operation algorithm (TA&CRFO) is proposed to realize semi-distributed control of the network. Simulation results show that both TARFC and TA&CRFO are effective in maintaining network connectivity and avoiding threats in dynamic scenarios. The average threat value of UAVs using TARFC and TA&CRFO is reduced by 3.99~27.51% and 3.07~26.63%, respectively, compared with the PSO algorithm. In addition, with limited distributed moderation, the complexity of the TA&CRFO algorithm is only 20.08% of that of TARFC. MDPI 2022-12-06 /pmc/articles/PMC9737455/ /pubmed/36502226 http://dx.doi.org/10.3390/s22239526 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
Guo, Yuan
Tang, Hongying
Qin, Ronghua
A Low Complexity Persistent Reconnaissance Algorithm for FANET
title A Low Complexity Persistent Reconnaissance Algorithm for FANET
title_full A Low Complexity Persistent Reconnaissance Algorithm for FANET
title_fullStr A Low Complexity Persistent Reconnaissance Algorithm for FANET
title_full_unstemmed A Low Complexity Persistent Reconnaissance Algorithm for FANET
title_short A Low Complexity Persistent Reconnaissance Algorithm for FANET
title_sort low complexity persistent reconnaissance algorithm for fanet
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9737455/
https://www.ncbi.nlm.nih.gov/pubmed/36502226
http://dx.doi.org/10.3390/s22239526
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