Delay constrained placement of mobile data collectors in underwater acoustic sensor networks
Bibliographic record
Abstract
We propose a scheme for routing and placement of mobile data collectors in Underwater Acoustic Sensor Networks (UASNs). The proposed scheme maximizes the lifetime of the network with an upper bound on the maximum delay. We assume a 3D architecture, in which on-the-surface data collectors gather data from underwater sensors and relay them to an on-shore sink. We divide the lifetime of the network into fixed length rounds and move the data collectors to new locations at the beginning of each round. This problem is formulated as an Integer Linear Program (ILP), and we use an ILP solver to find the optimal placement of data collectors together with the multi-hop routing paths to deliver data from underwater sensors to data collectors. To the best of our knowledge, this is the first attempt towards the placement of data collectors in a 3D environment with delay constraints. When compared with other schemes, our scheme has shown the capability to achieve longer lifetime and shorter delay.
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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".