Energy-Efficient Data Collection Using Autonomous Underwater Glider: A Reinforcement Learning Formulation Article Swipe
Xinbin Li
,
Xianglin Xu
,
Lei Yan
,
Haihong Zhao
,
Tongwei Zhang
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20133758
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.3390/s20133758
The autonomous underwater glider has attracted enormous interest for underwater activities, especially in long-term and large-scale underwater data collection. In this paper, we focus on the application of gliders gathering data from underwater sensor networks over underwater acoustic channels. However, this application suffers from a rapidly time-varying environment and limited energy. To optimize the performance of data collection and maximize the network lifetime, we propose a distributed, energy-efficient sensor scheduling algorithm based on the multi-armed bandit formulation. Besides, we design an indexable threshold policy to tradeoff between the data quality and the collection delay. Moreover, to reduce the computational complexity, we divide the proposed algorithm into off-line computation and on-line scheduling parts. Simulation results indicate that the proposed policy significantly improves the performance of the data collection and reduces the energy consumption. They prove the effectiveness of the threshold, which could reduce the collection delay by at least 10% while guaranteeing the data quality.
Related Topics
Concepts
Underwater glider
Data collection
Underwater
Computer science
Real-time computing
Glider
Computation
Energy consumption
Scheduling (production processes)
Efficient energy use
Reinforcement learning
Distributed computing
Engineering
Artificial intelligence
Algorithm
Operations management
Electrical engineering
Oceanography
Statistics
Geology
Mathematics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s20133758
- https://www.mdpi.com/1424-8220/20/13/3758/pdf?version=1594211828
- OA Status
- gold
- Cited By
- 14
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3039274205
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3039274205Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/s20133758Digital Object Identifier
- Title
-
Energy-Efficient Data Collection Using Autonomous Underwater Glider: A Reinforcement Learning FormulationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-07-04Full publication date if available
- Authors
-
Xinbin Li, Xianglin Xu, Lei Yan, Haihong Zhao, Tongwei ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/s20133758Publisher landing page
- PDF URL
-
https://www.mdpi.com/1424-8220/20/13/3758/pdf?version=1594211828Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/1424-8220/20/13/3758/pdf?version=1594211828Direct OA link when available
- Concepts
-
Underwater glider, Data collection, Underwater, Computer science, Real-time computing, Glider, Computation, Energy consumption, Scheduling (production processes), Efficient energy use, Reinforcement learning, Distributed computing, Engineering, Artificial intelligence, Algorithm, Operations management, Electrical engineering, Oceanography, Statistics, Geology, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
14Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 4, 2023: 1, 2022: 3, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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