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AUV-Aided Optical-Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
Fanfeng Bu1, Hanjiang Luo1, Saisai Ma1
1College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.
Sensors (Basel, Switzerland)
|January 21, 2023
Summary
This study introduces a new method for underwater data collection using autonomous underwater vehicles (AUVs) and multi-modal communication. The approach optimizes data freshness and energy efficiency through deep reinforcement learning, significantly reducing age of information and power consumption.
Area of Science:
- Robotics and Autonomous Systems
- Underwater Communications
- Wireless Sensor Networks
Background:
- Autonomous underwater vehicles (AUVs) offer flexible mobile data collection in underwater wireless sensor networks (UWSNs).
- Diverse applications require balancing data freshness, security, and energy efficiency.
Purpose of the Study:
- To propose a novel multi-modal AUV-assisted data collection scheme.
- To integrate acoustic and optical communication technologies for enhanced performance.
- To optimize data collection considering Age of Information (AoI), transmission energy, and AUV movement energy.
Main Methods:
- A multi-modal communication scheme integrating acoustic and optical technologies.
- A deep reinforcement learning (DRL) approach to determine optimal AUV motion trajectories.
- An optimal angle steering algorithm for AUV navigation to minimize energy consumption.
Main Results:
- The proposed scheme effectively balances data timeliness and energy efficiency.
- Simulations demonstrate significant reductions in the weighted sum of AoI.
- The approach also shows substantial decreases in overall energy consumption.
Conclusions:
- The novel multi-modal AUV-assisted scheme enhances data collection in UWSNs.
- DRL and optimal angle steering are effective in optimizing AoI and energy usage.
- The integrated approach provides a timely and reliable solution for underwater data retrieval.
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