Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning

Detalhes bibliográficos
Autor(a) principal: Li, Kai
Data de Publicação: 2022
Outros Autores: Ni, Wei, Emami, Yousef, Dressler, Falko
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.22/21642
Resumo: Energy-harvesting-powered sensors are increasingly deployed beyond the reach of terrestrial gateways, where there is often no persistent power supply. Making use of the internet of drones (IoD) for data aggregation in such environments is a promising paradigm to enhance network scalability and connectivity. The flexibility of IoD and favorable line-of-sight connections between the drones and ground nodes are exploited to improve data reception at the drones. In this article, we discuss the challenges of online flight control of IoD, where data-driven neural networks can be tailored to design the trajectories and patrol speeds of the drones and their communication schedules, preventing buffer overflows at the ground nodes. In a small-scale IoD, a multi-agent deep reinforcement learning can be developed with long short-term memory to train the continuous flight control of IoD and data aggregation scheduling, where a joint action is generated for IoD via sharing the flight control decisions among the drones. In a large-scale IoD, sharing the flight control decisions in real-time can result in communication overheads and interference. In this case, deep reinforcement learning can be trained with the second-hand visiting experiences, where the drones learn the actions of each other based on historical scheduling records maintained at the ground nodes.
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spelling Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning220502Internet of dronesMulti-agent deep reinforcement learningFlight controlData aggregationLong short-term memoryEnergy-harvesting-powered sensors are increasingly deployed beyond the reach of terrestrial gateways, where there is often no persistent power supply. Making use of the internet of drones (IoD) for data aggregation in such environments is a promising paradigm to enhance network scalability and connectivity. The flexibility of IoD and favorable line-of-sight connections between the drones and ground nodes are exploited to improve data reception at the drones. In this article, we discuss the challenges of online flight control of IoD, where data-driven neural networks can be tailored to design the trajectories and patrol speeds of the drones and their communication schedules, preventing buffer overflows at the ground nodes. In a small-scale IoD, a multi-agent deep reinforcement learning can be developed with long short-term memory to train the continuous flight control of IoD and data aggregation scheduling, where a joint action is generated for IoD via sharing the flight control decisions among the drones. In a large-scale IoD, sharing the flight control decisions in real-time can result in communication overheads and interference. In this case, deep reinforcement learning can be trained with the second-hand visiting experiences, where the drones learn the actions of each other based on historical scheduling records maintained at the ground nodes.This work was supported in part by the National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit under Grant UIDP/UIDB/04234/2020, and in part by the National Funds through FCT, under CMU Portugal Partnership under Project CMU/TIC/0022/2019 (CRUAV).IEEERepositório Científico do Instituto Politécnico do PortoLi, KaiNi, WeiEmami, YousefDressler, Falko2023-01-18T11:51:07Z2022-05-062022-05-06T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/21642eng10.1109/MWC.002.2100681info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-03-13T13:17:29Zoai:recipp.ipp.pt:10400.22/21642Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:41:38.415826Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning
220502
title Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning
spellingShingle Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning
Li, Kai
Internet of drones
Multi-agent deep reinforcement learning
Flight control
Data aggregation
Long short-term memory
title_short Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning
title_full Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning
title_fullStr Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning
title_full_unstemmed Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning
title_sort Data-driven Flight Control of Internet-of- Drones for Sensor Data Aggregation using Multi-agent Deep Reinforcement Learning
author Li, Kai
author_facet Li, Kai
Ni, Wei
Emami, Yousef
Dressler, Falko
author_role author
author2 Ni, Wei
Emami, Yousef
Dressler, Falko
author2_role author
author
author
dc.contributor.none.fl_str_mv Repositório Científico do Instituto Politécnico do Porto
dc.contributor.author.fl_str_mv Li, Kai
Ni, Wei
Emami, Yousef
Dressler, Falko
dc.subject.por.fl_str_mv Internet of drones
Multi-agent deep reinforcement learning
Flight control
Data aggregation
Long short-term memory
topic Internet of drones
Multi-agent deep reinforcement learning
Flight control
Data aggregation
Long short-term memory
description Energy-harvesting-powered sensors are increasingly deployed beyond the reach of terrestrial gateways, where there is often no persistent power supply. Making use of the internet of drones (IoD) for data aggregation in such environments is a promising paradigm to enhance network scalability and connectivity. The flexibility of IoD and favorable line-of-sight connections between the drones and ground nodes are exploited to improve data reception at the drones. In this article, we discuss the challenges of online flight control of IoD, where data-driven neural networks can be tailored to design the trajectories and patrol speeds of the drones and their communication schedules, preventing buffer overflows at the ground nodes. In a small-scale IoD, a multi-agent deep reinforcement learning can be developed with long short-term memory to train the continuous flight control of IoD and data aggregation scheduling, where a joint action is generated for IoD via sharing the flight control decisions among the drones. In a large-scale IoD, sharing the flight control decisions in real-time can result in communication overheads and interference. In this case, deep reinforcement learning can be trained with the second-hand visiting experiences, where the drones learn the actions of each other based on historical scheduling records maintained at the ground nodes.
publishDate 2022
dc.date.none.fl_str_mv 2022-05-06
2022-05-06T00:00:00Z
2023-01-18T11:51:07Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/21642
url http://hdl.handle.net/10400.22/21642
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1109/MWC.002.2100681
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dc.publisher.none.fl_str_mv IEEE
publisher.none.fl_str_mv IEEE
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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