T-SAPR: An Efficient Q-Learning Trust-based Secure Routing Protocol for Underwater Acoustic Sensor Networks
Bibliographic record
Abstract
Underwater acoustic sensor networks (UASNs) play pivotal roles in diverse civilian and military contexts. Yet, due to their broadcasting nature and the challenging environments they operate in, they are susceptible to a multitude of security vulnerabilities. Additionally, few existing routing protocols in UASNs account for both security and underwater transmission challenges. In response, we introduce T-SAPR, a secure Q-Learning-based routing protocol bolstered by trust management and AUV-driven path restoration. T-SAPR utilizes an attention-based Long Short-Term Memory (LSTM) to construct a multifaceted trust model, encompassing node trust, communication trust, and environmental trust. Further, energy, packet delivery ratio, and latency collectively inform optimal routing strategies via Q-Learning's reward function. Additionally, an AUV-assisted repair mechanism to enhance UASNs' reliability is proposed, particularly in scenarios involving multiple sensor node failures or the detection of malicious nodes. Evaluation results demonstrate T-SAPR's prowess in identifying malicious nodes.
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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 | 0.000 | 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 | 0.000 |
| 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".