Placement of multiple mobile data collectors in underwater acoustic sensor networks
Bibliographic record
Abstract
Abstract We propose two schemes for routing and placement of mobile data collectors in underwater acoustic sensor networks (UASNs): the delay tolerant placement and routing (DTPR) and the delay constrained placement and routing (DCPR). As their names reveal, while the DTPR maximizes the lifetime of the network without any delay considerations, the DCPR maximizes the lifetime of the network with an upper bound on the maximum delay. Both schemes are based on 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. These problems are formulated as integer linear programs (ILPs), 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. Our work is a pioneering effort in the placement of mobile data collectors in three‐dimensional UASNs. Comprehensive experiments show that our schemes prolong the lifetime of the network significantly as compared to other data collector placement schemes. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".