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Energy-Efficient Data Collection Using Autonomous Underwater Glider: A Reinforcement Learning Formulation.

Xinbin Li¹, Xianglin Xu¹, Lei Yan¹

¹Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.

Sensors (Basel, Switzerland)

|July 9, 2020

Summary

This summary is machine-generated.

This study introduces an energy-efficient sensor scheduling algorithm for autonomous underwater gliders. The proposed method optimizes data collection and extends network lifetime by balancing data quality and collection delay.

Keywords:

autonomous underwater glider energy efficiency indexable threshold policy multi-armed bandit underwater data collection

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Area of Science:

Robotics
Oceanography
Sensor Networks

Background:

Autonomous underwater gliders are crucial for long-term, large-scale underwater data collection.
Collecting data via underwater sensor networks over acoustic channels faces challenges from time-varying environments and limited energy.

Purpose of the Study:

To propose a distributed, energy-efficient sensor scheduling algorithm for underwater gliders.
To optimize data collection performance and maximize network lifetime.
To balance data quality and collection delay.

Main Methods:

Developed a distributed, energy-efficient sensor scheduling algorithm using a multi-armed bandit formulation.
Designed an indexable threshold policy to manage the tradeoff between data quality and collection delay.
Implemented an off-line computation and on-line scheduling approach to reduce complexity.

Main Results:

The proposed policy significantly enhances data collection performance.
The algorithm effectively reduces energy consumption in underwater sensor networks.
The threshold policy demonstrated a reduction in collection delay by at least 10% while maintaining data quality.

Conclusions:

The developed sensor scheduling algorithm is effective for autonomous underwater gliders.
The proposed threshold policy successfully optimizes data collection efficiency and network longevity.
This approach offers a practical solution for energy-constrained underwater data gathering.