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Completion Time Minimization for UAV-UGV-Enabled Data Collection

In unmanned aerial vehicle (UAV)-enabled data collection systems, situations where sensor nodes (SNs) cannot upload their data successfully to the UAV may exist, due to factors such as SNs’ insufficient energy and the UAV’s minimum flight altitude. In this paper, an unmanned ground vehicle (UGV)-UAV...

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Autores principales: Li, Zhijian, Zhao, Wendong, Liu, Cuntao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370958/
https://www.ncbi.nlm.nih.gov/pubmed/35957401
http://dx.doi.org/10.3390/s22155839
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author Li, Zhijian
Zhao, Wendong
Liu, Cuntao
author_facet Li, Zhijian
Zhao, Wendong
Liu, Cuntao
author_sort Li, Zhijian
collection PubMed
description In unmanned aerial vehicle (UAV)-enabled data collection systems, situations where sensor nodes (SNs) cannot upload their data successfully to the UAV may exist, due to factors such as SNs’ insufficient energy and the UAV’s minimum flight altitude. In this paper, an unmanned ground vehicle (UGV)-UAV-enabled data collection system is studied, where data collection missions are conducted by a UAV and a UGV cooperatively. Two cooperative strategies are proposed, i.e., collaboration without information interaction, and collaboration with information interaction. In the first strategy, the UGV collects data from remote SNs (i.e., the SNs that cannot upload data to the UAV) as well as some normal SNs (i.e., the SNs that can upload data to the UAV), while the UAV only collects data from some normal SNs. Then, they carry the data back to the data center (DC) without interacting with each other. In the second strategy, the UGV only collects data from remote SNs, while transmitting the collected data to the UAV at a data interaction point, then the data are carried back to the DC by the UAV. There are mobile data collection nodes on the ground and in the air, and the task is to find trajectories to minimize the data collection time in the data center. A collaborative strategy selection algorithm, combining a multi-stage-based SN association and UAV-UGV path optimization algorithm, is proposed to solve the problem effectively, where techniques including convex optimization and genetic algorithm are adopted. The simulation result shows that the proposed scheme reduces the mission completion time by 36% compared with the benchmark scheme.
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spelling pubmed-93709582022-08-12 Completion Time Minimization for UAV-UGV-Enabled Data Collection Li, Zhijian Zhao, Wendong Liu, Cuntao Sensors (Basel) Article In unmanned aerial vehicle (UAV)-enabled data collection systems, situations where sensor nodes (SNs) cannot upload their data successfully to the UAV may exist, due to factors such as SNs’ insufficient energy and the UAV’s minimum flight altitude. In this paper, an unmanned ground vehicle (UGV)-UAV-enabled data collection system is studied, where data collection missions are conducted by a UAV and a UGV cooperatively. Two cooperative strategies are proposed, i.e., collaboration without information interaction, and collaboration with information interaction. In the first strategy, the UGV collects data from remote SNs (i.e., the SNs that cannot upload data to the UAV) as well as some normal SNs (i.e., the SNs that can upload data to the UAV), while the UAV only collects data from some normal SNs. Then, they carry the data back to the data center (DC) without interacting with each other. In the second strategy, the UGV only collects data from remote SNs, while transmitting the collected data to the UAV at a data interaction point, then the data are carried back to the DC by the UAV. There are mobile data collection nodes on the ground and in the air, and the task is to find trajectories to minimize the data collection time in the data center. A collaborative strategy selection algorithm, combining a multi-stage-based SN association and UAV-UGV path optimization algorithm, is proposed to solve the problem effectively, where techniques including convex optimization and genetic algorithm are adopted. The simulation result shows that the proposed scheme reduces the mission completion time by 36% compared with the benchmark scheme. MDPI 2022-08-04 /pmc/articles/PMC9370958/ /pubmed/35957401 http://dx.doi.org/10.3390/s22155839 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
Li, Zhijian
Zhao, Wendong
Liu, Cuntao
Completion Time Minimization for UAV-UGV-Enabled Data Collection
title Completion Time Minimization for UAV-UGV-Enabled Data Collection
title_full Completion Time Minimization for UAV-UGV-Enabled Data Collection
title_fullStr Completion Time Minimization for UAV-UGV-Enabled Data Collection
title_full_unstemmed Completion Time Minimization for UAV-UGV-Enabled Data Collection
title_short Completion Time Minimization for UAV-UGV-Enabled Data Collection
title_sort completion time minimization for uav-ugv-enabled data collection
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9370958/
https://www.ncbi.nlm.nih.gov/pubmed/35957401
http://dx.doi.org/10.3390/s22155839
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AT liucuntao completiontimeminimizationforuavugvenableddatacollection