An Adaptive Asynchronous Wake-Up Scheme for Underwater Acoustic Sensor Networks Using Deep Reinforcement Learning
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Bibliographic record
Abstract
Underwater acoustic sensor networks (UWSNs), acting as a reliable and efficient infrastructure of the Internet of underwater things (IoUT), have attracted much research interest in recent years due to the wide range of their potential marine applications. The limited energy supply of underwater sensor nodes is a significant challenge that can be mitigated by the cyclic difference set (CDS)-based coordination asynchronous wake-up scheme. However, the CDS-based asynchronous wake-up scheme also introduces long delays in the neighbor discovery that deteriorates packet delay as well as the network lifetime. In this paper, we formulate the problem of policy selection for idle listening as a Markov decision process and exploit the framework of deep reinforcement learning to obtain the optimal policies of underwater sensor nodes. Furthermore, the long short-term memory (LSTM) networks are utilized to estimate the network traffic feature, which can improve the performance of the proposed adaptive asynchronous wake-up scheme. To verify the performance of the proposed scheme, simulations in different network scenarios are conducted with the comparison of random, fixed metric policies, and original CDS-based asynchronous wake-up schemes.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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