Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks

Detalhes bibliográficos
Autor(a) principal: Li, Kai
Data de Publicação: 2021
Outros Autores: Ni, Wei, 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/10400.22/21906
Resumo: Thanks to flexible deployment and excellent maneuverability, autonomous drones are regarded as an effective means to enable aerial data capture in large-scale wireless sensor networks with limited to no cellular infrastructure, e.g., smart farming in a remote area. A key challenge in drone-assisted sensor networks is that the autonomous drone's maneuvering can give rise to buffer overflows at the ground sensors and unsuccessful data collection due to lossy airborne channels. In this paper, we propose a new Deep Deterministic Policy Gradient based Maneuver Control (DDPG-MC) scheme which minimizes the overall data packet loss through online training instantaneous headings and patrol velocities of the drone, and the selection of the ground sensors for data collection in a continuous action space. Moreover, the maneuver control of the drone and communication schedule is formulated as an absorbing Markov chain, where network states consist of battery energy levels, data queue backlogs, timestamps of the data collection, and channel conditions between the ground sensors and the drone. An experience replay memory is utilized onboard at the drone to store the training experiences of the maneuver control and communication schedule at each time step.
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spelling Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks210101Autonomous droneManeuver controlData collectionDeep reinforcement learningAbsorbing Markov chainThanks to flexible deployment and excellent maneuverability, autonomous drones are regarded as an effective means to enable aerial data capture in large-scale wireless sensor networks with limited to no cellular infrastructure, e.g., smart farming in a remote area. A key challenge in drone-assisted sensor networks is that the autonomous drone's maneuvering can give rise to buffer overflows at the ground sensors and unsuccessful data collection due to lossy airborne channels. In this paper, we propose a new Deep Deterministic Policy Gradient based Maneuver Control (DDPG-MC) scheme which minimizes the overall data packet loss through online training instantaneous headings and patrol velocities of the drone, and the selection of the ground sensors for data collection in a continuous action space. Moreover, the maneuver control of the drone and communication schedule is formulated as an absorbing Markov chain, where network states consist of battery energy levels, data queue backlogs, timestamps of the data collection, and channel conditions between the ground sensors and the drone. An experience replay memory is utilized onboard at the drone to store the training experiences of the maneuver control and communication schedule at each time step.This work was supported in part by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (UIDB/ 04234/2020), also by the Operational Competitiveness Programme and Internationalization (COMPETE 2020) under the PT2020 Partnership Agreement, through the European Regional Development Fund (ERDF), and by national funds through the FCT, within project(s) POCI-01-0145-FEDER029074 (ARNET).IEEERepositório Científico do Instituto Politécnico do PortoLi, KaiNi, WeiDressler, Falko2021-01-052035-01-01T00:00:00Z2021-01-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.22/21906eng10.1109/TMC.2021.3049178metadata only accessinfo: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:RCAAP2023-03-13T13:18:14Zoai:recipp.ipp.pt:10400.22/21906Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T17:41:58.904473Repositório 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 Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks
210101
title Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks
spellingShingle Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks
Li, Kai
Autonomous drone
Maneuver control
Data collection
Deep reinforcement learning
Absorbing Markov chain
title_short Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks
title_full Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks
title_fullStr Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks
title_full_unstemmed Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks
title_sort Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks
author Li, Kai
author_facet Li, Kai
Ni, Wei
Dressler, Falko
author_role author
author2 Ni, Wei
Dressler, Falko
author2_role 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
Dressler, Falko
dc.subject.por.fl_str_mv Autonomous drone
Maneuver control
Data collection
Deep reinforcement learning
Absorbing Markov chain
topic Autonomous drone
Maneuver control
Data collection
Deep reinforcement learning
Absorbing Markov chain
description Thanks to flexible deployment and excellent maneuverability, autonomous drones are regarded as an effective means to enable aerial data capture in large-scale wireless sensor networks with limited to no cellular infrastructure, e.g., smart farming in a remote area. A key challenge in drone-assisted sensor networks is that the autonomous drone's maneuvering can give rise to buffer overflows at the ground sensors and unsuccessful data collection due to lossy airborne channels. In this paper, we propose a new Deep Deterministic Policy Gradient based Maneuver Control (DDPG-MC) scheme which minimizes the overall data packet loss through online training instantaneous headings and patrol velocities of the drone, and the selection of the ground sensors for data collection in a continuous action space. Moreover, the maneuver control of the drone and communication schedule is formulated as an absorbing Markov chain, where network states consist of battery energy levels, data queue backlogs, timestamps of the data collection, and channel conditions between the ground sensors and the drone. An experience replay memory is utilized onboard at the drone to store the training experiences of the maneuver control and communication schedule at each time step.
publishDate 2021
dc.date.none.fl_str_mv 2021-01-05
2021-01-05T00:00:00Z
2035-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.22/21906
url http://hdl.handle.net/10400.22/21906
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1109/TMC.2021.3049178
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eu_rights_str_mv openAccess
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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)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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