Continuous Maneuver Control and Data Capture Scheduling of Autonomous Drone in Wireless Sensor Networks
Autor(a) principal: | |
---|---|
Data de Publicação: | 2021 |
Outros Autores: | , |
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. |
id |
RCAP_43ce5cec2fd8e1265044863576246efd |
---|---|
oai_identifier_str |
oai:recipp.ipp.pt:10400.22/21906 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
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 |
format |
article |
status_str |
publishedVersion |
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 |
dc.rights.driver.fl_str_mv |
metadata only access info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
metadata only access |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
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 instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799131506238554112 |