Bibliographic record
Abstract
Underwater sensor networks have become increasingly interesting in the past four decades. They can be used in a multitude of scenarios, commercial and military alike. Underwater networks can communicate in several ways, but when nodes are far apart, underwater acoustic communication is the only feasible way. The complex underwater acoustic channel puts high demands on the network protocols. The physical layer needs to contend with short coherence times, high intersymbol interference and significant Doppler spread. The routing protocol needs to handle intermittent connectivity and mobile network topologies, such as autonomous underwater vehicle networks. The medium access control protocol needs to manage medium access with high latency and potentially high packet loss ratios without congesting the network. The available acoustic modems are still rather expensive, which limits the size of a sensor network. Voices have also been raised from the academia for a paradigm shift, from hardware-defined, proprietary modems to software-defined, open-architecture modems, in order to accelerate research in the field and enable interoperability. This paper reviews the recent advancements in designing and implementing underwater networks on several levels and discusses some interesting approaches to underwater ad-hoc networking. The focus lies on acoustic communication.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".