Robust grid-based deployment schemes for underwater optical sensor networks
Bibliographic record
Abstract
Underwater sensor networks have received significant attention from the research community in recent years. Since radio signals face excessive absorption in the underwater environment, acoustic communication has been the dominant physical layer medium in the literature. Although acoustic communication has long range and omni-directional characteristics like terrestrial radio, it suffers from excessive propagation delay in water and very low bandwidth. In this paper, we consider the design of an optical underwater sensor network based on low cost LEDs and photodiodes. Such an optical communication system has shorter range compared to acoustic systems but is cheaper and can support significantly higher bandwidth. Optical communication requires line of sight which makes optical links vulnerable to occasional failures due to underwater organisms and moving particles. We consider a grid based deployment of underwater sensor nodes and the selection of a topology based on point-to-point optical links that is robust to occasional link failures. We develop patterns for networks with at most 3 interfaces per node constraints. We evaluate the robustness of our proposed deployment patterns by simulating three resilient routing protocols on these patterns and demonstrate that our patterns support a high degree of robustness even though they use only a fraction of all potential links in the grid graph.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".