Efficient Data Collection Scheme for Multi-Modal Underwater Sensor Networks Based on Deep Reinforcement Learning
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Bibliographic record
Abstract
Autonomous Underwater Vehicles (AUVs) with multi-modal transmission can achieve high efficient data collection for underwater sensor networks. However, multi-modal transmission and trajectory planning impose great challenges on data collection in complex underwater environments. Most prior studies focus on design of multi-modal architecture, but lack of available implementation and consideration of AUVs' trajectory. Meanwhile, existing trajectory planning research cannot work well on data collection with multiple complex tasks simultaneously. In this paper, an efficient Data Collection scheme for Multi-modal underwater sensor networks based on Deep reinforcement learning (DCMD) is proposed to solve the above challenges. We first propose an optimal multi-modal transmission selection algorithm that provides an implementation to improve transmission efficiency. Then we propose a distributed multi-AUVs' trajectory planning algorithm based on deep reinforcement learning by AUVs' collaborations, considering transmission situation, ocean currents and underwater obstacles, to maximize collection rate and energy efficiency. In addition, we joint transmission and trajectory planning in a protocol to improve collection efficiency. Extensive experimental results show that DCMD achieves better performance on efficiency and reliability than four state-of-the-art methods, demonstrating its great advantage on collecting data for USNs.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it