Underwater Wireless Sensor Networks
Bibliographic record
Abstract
Underwater wireless sensor networks (UWSNs) will pave the way for a new era of underwater monitoring and actuation applications. The envisioned landscape of UWSN applications will help us learn more about our oceans, as well as about what lies beneath them. They are expected to change the current reality where no more than 5% of the volume of the oceans has been observed by humans. However, to enable large deployments of UWSNs, networking solutions toward efficient and reliable underwater data collection need to be investigated and proposed. In this context, the use of topology control algorithms for a suitable, autonomous, and on-the-fly organization of the UWSN topology might mitigate the undesired effects of underwater wireless communications and consequently improve the performance of networking services and protocols designed for UWSNs. This article presents and discusses the intrinsic properties, potentials, and current research challenges of topology control in underwater sensor networks. We propose to classify topology control algorithms based on the principal methodology used to change the network topology. They can be categorized in three major groups: power control, wireless interface mode management, and mobility assisted–based techniques. Using the proposed classification, we survey the current state of the art and present an in-depth discussion of topology control solutions designed for UWSNs.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".