Performance Evaluation of Candidate Set Selection Procedures for Underwater Sensor Networks
Bibliographic record
Abstract
In recent years, the opportunistic routing (OR) paradigm has been shown as one of the most viable solutions at the network layer for efficient data delivery under the nosily underwater acoustic channel. Since then, several OR protocols for underwater sensor networks, with major and minor variations, were proposed in the literature. While the performance of these protocols, in terms of data delivery rate, delay, and energy consumption, has been extensively studied in the literature, there is a lack of studies devoted for the evaluation of the topological properties OR will incur. In this paper, we evaluate the performance of the candidate set selection procedures of opportunistic routing protocols designed for underwater sensor networks. We discuss the principles that have been extensively considered for the design of OR protocols for underwater sensor networks. Moreover, we conduct a simulation-based performance evaluation to study topological properties (e.g., number of hops, number of paths and number of candidates), which will impact the performance of underwater wireless sensor network applications.
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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".