A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things

[EN] Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the...

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Autores: Zerrougui, Sabrina, Zaidi, Sofiane, Tavares De Araujo Cesariny Calafate, Carlos Miguel|||0000-0001-5729-3041
Formato: artículo
Fecha de publicación:2025
País:España
Recursos:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/230298
Acesso em linha:https://riunet.upv.es/handle/10251/230298
Access Level:acceso abierto
Palavra-chave:Particle swarm optimization
UAV deployment
Multi-objective optimization
Epsilon constraint
Pareto archive
Weighted sum
Internet of Flying Things
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spelling A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying ThingsZerrougui, SabrinaZaidi, SofianeTavares De Araujo Cesariny Calafate, Carlos Miguel|||0000-0001-5729-3041Particle swarm optimizationUAV deploymentMulti-objective optimizationEpsilon constraintPareto archiveWeighted sumInternet of Flying Things[EN] Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the deployment of edge-enabled UAVs using Pareto-PSO is proposed for data collection scenarios in which UAVs operate autonomously and execute onboard distributed multi-objective PSO to maximize the total non-overlapping coverage area while minimizing latency and energy consumption. Performance evaluation is conducted using key indicators, including convergence time, throughput, and total non-overlapping coverage area across bandwidth and swarm-size sweeps. Simulation results demonstrate that the Pareto-PSO consistently attains the highest throughput and the largest coverage envelope, while exhibiting moderate and scalable convergence times. These results highlight the advantage of treating the objectives as a vector-valued objective in Pareto-PSO for real-time, scalable, and energy-aware edge-UAV deployment in dynamic Internet of Flying Things environments.This work was partially funded by the EU project REMARKABLE (Grant agreement ID: 101086387), under the program HORIZON-MSCA-2021-SE-01-01, by the project CIPROM/2023/29, which is funded by "Direccio General de Ciencia i Investigacio", Generalitat Valenciana, Spain, by R&D project PID2021-122580NB-I00, from MICIU/AEI/10.13039/501100011033, and "ERDF A way of making Europe".MDPI AGDepartamento de Informática de Sistemas y ComputadoresEscuela Técnica Superior de Ingeniería InformáticaGrupo de Redes de ComputadoresEuropean CommissionGeneralitat ValencianaAgencia Estatal de InvestigaciónEuropean Regional Development FundRepositorio Institucional de la Universitat Politècnica de València Riunet20252025-10-24journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/230298reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-122580NB-I00 SISTEMAS INTELIGENTES DE SENSORIZACION PARA ECOSISTEMAS, ESPACIOS URBANOS Y MOVILIDAD SOSTENIBLEEuropean Commission https://doi.org/10.13039/501100000780 HE 101086387 Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated LearningGeneralitat Valenciana https://doi.org/10.13039/501100003359 CIPROM%2F2023%2F29 BRIDGING AI WITH IOT TO PROVIDE ENVIRONMENTAL INTELLIGENCE: A COMPREHENSIVE APPROACH TO SUSTAINABLE MONITORING AND DATA-DRIVEN DECISION MAKINGopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2302982026-06-13T07:49:27Z
dc.title.none.fl_str_mv A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things
title A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things
spellingShingle A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things
Zerrougui, Sabrina
Particle swarm optimization
UAV deployment
Multi-objective optimization
Epsilon constraint
Pareto archive
Weighted sum
Internet of Flying Things
title_short A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things
title_full A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things
title_fullStr A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things
title_full_unstemmed A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things
title_sort A Resilient Distributed Pareto-Based PSO for Edge-UAVs Deployment Optimization in Internet of Flying Things
dc.creator.none.fl_str_mv Zerrougui, Sabrina
Zaidi, Sofiane
Tavares De Araujo Cesariny Calafate, Carlos Miguel|||0000-0001-5729-3041
author Zerrougui, Sabrina
author_facet Zerrougui, Sabrina
Zaidi, Sofiane
Tavares De Araujo Cesariny Calafate, Carlos Miguel|||0000-0001-5729-3041
author_role author
author2 Zaidi, Sofiane
Tavares De Araujo Cesariny Calafate, Carlos Miguel|||0000-0001-5729-3041
author2_role author
author
dc.contributor.none.fl_str_mv Departamento de Informática de Sistemas y Computadores
Escuela Técnica Superior de Ingeniería Informática
Grupo de Redes de Computadores
European Commission
Generalitat Valenciana
Agencia Estatal de Investigación
European Regional Development Fund
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Particle swarm optimization
UAV deployment
Multi-objective optimization
Epsilon constraint
Pareto archive
Weighted sum
Internet of Flying Things
topic Particle swarm optimization
UAV deployment
Multi-objective optimization
Epsilon constraint
Pareto archive
Weighted sum
Internet of Flying Things
description [EN] Particle Swarm Optimization (PSO) has been widely employed to optimize the deployment of Unmanned Aerial Vehicles (UAVs) in various scenarios, particularly because of its efficiency in handling both single and multi-objective optimization problems. In this paper, a framework for optimizing the deployment of edge-enabled UAVs using Pareto-PSO is proposed for data collection scenarios in which UAVs operate autonomously and execute onboard distributed multi-objective PSO to maximize the total non-overlapping coverage area while minimizing latency and energy consumption. Performance evaluation is conducted using key indicators, including convergence time, throughput, and total non-overlapping coverage area across bandwidth and swarm-size sweeps. Simulation results demonstrate that the Pareto-PSO consistently attains the highest throughput and the largest coverage envelope, while exhibiting moderate and scalable convergence times. These results highlight the advantage of treating the objectives as a vector-valued objective in Pareto-PSO for real-time, scalable, and energy-aware edge-UAV deployment in dynamic Internet of Flying Things environments.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025-10-24
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/230298
url https://riunet.upv.es/handle/10251/230298
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023 PID2021-122580NB-I00 SISTEMAS INTELIGENTES DE SENSORIZACION PARA ECOSISTEMAS, ESPACIOS URBANOS Y MOVILIDAD SOSTENIBLE
European Commission https://doi.org/10.13039/501100000780 HE 101086387 Rural Environmental Monitoring via ultra wide-ARea networKs And distriButed federated Learning
Generalitat Valenciana https://doi.org/10.13039/501100003359 CIPROM%2F2023%2F29 BRIDGING AI WITH IOT TO PROVIDE ENVIRONMENTAL INTELLIGENCE: A COMPREHENSIVE APPROACH TO SUSTAINABLE MONITORING AND DATA-DRIVEN DECISION MAKING
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,198674

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