A Dependable Clustering Protocol for Survivable Underwater Sensor Networks
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Bibliographic record
Abstract
Node clustering has been widely considered in underwater sensor networks (UWSNs) to improve energy efficiency and prolong network lifetime. Network survivability is a great concern in cluster-based UWSNs. In this paper, we propose a dependable clustering protocol to provide a survivable cluster hierarchy against cluster-head failures in such networks. The proposed clustering protocol attempts to select a primary cluster head and a backup cluster head during clustering so that the cluster members associated with the failed cluster head can quickly switch over to the backup cluster head in the event of a cluster-head failure. Meanwhile, it attempts to select a set of clusters with minimum total cost so that network lifetime can be prolonged to ensure long-term underwater environmental monitoring. Simulation results show that the protocol can effectively enhance network survivability and improve network capacity in the event of cluster-head failures.
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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 | 0.000 |
| 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 it