Efficient Collision-Free Data Collection for Underwater Acoustic Sensor Networks: A Hierarchical DRL Approach
Bibliographic record
Abstract
Autonomous underwater vehicles (AUVs) have become a promising solution for data collection in underwater acoustic sensor networks (UASNs), and deep reinforcement learning (DRL) has been widely applied to enhance collection performance. However, our preliminary experiments indicate that existing DRL-based studies still face two critical challenges: 1) Collection blind spots. The sparse collection rewards and the requirement for energy-efficient trajectory planning during data collection jointly restrict AUVs' ability to explore and collect data from all sensor nodes (SNs), ultimately resulting in some SNs remaining uncollected. 2) Collection collisions. Simultaneous data collection by multiple AUVs can lead to packet collisions and collection failures, further decreasing the collection rate. To address these challenges, we propose a <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">h</u>ierarchical DRL-based <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</u>ollision-free <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">d</u>ata <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</u>ollection scheme (HCDC). Specifically, we leverage a hierarchical DRL framework to decompose the multi-AUV-assisted data collection (MADC) problem into a high-level global target selection (GTS) and a low-level local trajectory planning (LTP) subproblems. For GTS, we design a multi-agent GTS (MA-GTS) algorithm to assign the next target SN for collection to each AUV. The MA-GTS incorporates both global and local rewards to collaboratively optimize the overall energy consumption while avoiding individual penalties. Based on the assigned target SN, a deep deterministic policy gradient-based LTP (DDPG-LTP) algorithm is proposed to conduct AUV trajectory planning, utilizing intrinsic rewards to enhance learning efficiency and eliminate collection blind spots. Furthermore, to avoid packet collisions, we analyze the conditions for collision-free data collection and propose an adaptive back-off slot (ABS) algorithm to schedule AUVs' collection slots. With the collision-free slots, DDPG-LTP dynamically adjusts AUVs' velocities to ensure collision-free collection while reducing energy consumption. Extensive simulation results demonstrate that HCDC can achieve better collection rate and energy efficiency than state-of-the-art schemes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".