A Novel Centrality Metric for Topology Control in Underwater Sensor Networks
Bibliographic record
Abstract
In underwater sensor networks, the design of energy efficient and reliable data collection protocols is a daunting challenge. In this context, topology control and opportunistic routing are promising techniques for improving reliability and conserve energy. However, due to the challenges of the underwater acoustic channel, the vast knowledge acquired and the solution proposed so far in the context of terrestrial wireless ad hoc sensor networks cannot be applied directly to underwater acoustic sensor networks. In this work, we shed light on network topology modeling from a routing viewpoint. We model the probabilistic multipath routing behavior driven by opportunistic routing protocols in underwater sensor networks. Afterward, we propose the PCen centrality metric to measure the importance of underwater sensor nodes to the data delivery task through opportunistic routing protocols. PCen is aimed to identify critical nodes that can be used to guide topology control solutions. Our simulation results consider different network densities and reveal the presence of a few number of nodes with high PCen centrality value that will have a high rate of carried traffic, being critical for the network performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".