Bibliographic record
Abstract
Underwater sensor networks (USNs) promise innovative and exciting applications, viz. oceanographic data collection, environment monitoring, exploration, and tactical surveillance. Underwater wireless sensor networks (UWSNs) though pose significant research challenges due to the harsh underwater environment. In UWSNs, acoustic is thought to be the only viable means of communication. Underwater wireless acoustic sensor networks (UW-ASNs) present a wireless channel with key challenges, specifically in shallow oceans such as long propagation delays, signal attenuation, man-made and ambient noise, low bandwidth and high transmission energy. We propose a new paradigm for UWSNs, namely underwater wireless hybrid sensor networks (UW-HSNs), which introduce the concept of hybrid communication. UW-HSNs combine the best of both worlds, i.e., the practicality of underwater acoustics and the high-performance of radio communication. The basic idea is to use radio communication for large and/or sustained traffic and traditional acoustic methods for small data volume. Furthermore, we introduce TurtleNet, an architecture based-on UW-HSNs concept, and we propose an asynchronous and distributed routing protocol for TurtleNet. Based on the nodepsilas state, the protocol decides which communication channel to utilize. TurtleNet is simulated using the ns-2 simulator. Simulation results reveal the promising performance for TurtleNet, and hence validate the UW-HSNs concept.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".