Localization and Data Collection in AUV-Aided Underwater Sensor Networks: Challenges and Opportunities
Bibliographic record
Abstract
With the fast growing demand for underwater applications such as marine environmental monitoring, undersea resource exploration, disaster prevention and monitoring, assisted localization and navigation, and security monitoring, the IoUT is proposed to enable a new network framework to connect underwater smart things in rivers and oceans. Conventional UWSNs are perceived as the fundamental infrastructure of IoUT. However, the high cost of underwater devices, high energy consumption of data aggregation, and low localization accuracy limit their further development. AUV brings the mobility property into network designs, which improves localization accuracy, data transmission rate, and data aggregation efficiency. However, it also brings new challenges for localization, path planning and coordination of AUVs. In this article, we briefly introduce the architecture of AUV-aided UWSNs and summarize their advantages based on the current research. Several significant issues when designing localization algorithms and coordination schemes for AUV-aided UWSNs are investigated in detail. We also analyze the main challenges under different scenarios such as the interaction between AUV and sensor nodes and communications among multiple AUVs. Based on these discussions, we conclude the article with the future research directions of AUV-aided 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".