MobiL-AUV: AUV-Aided Localization Scheme for Underwater Wireless Sensor Networks
Bibliographic record
Abstract
In this paper, we present the mobile autonomous underwater vehicle (AUV)-aided efficient localization scheme for underwater wireless sensor networks (UWSNs). Localization is one of the major issues in UWSNs as it is important in some large scale applications to know the accurate position of sensor nodes. It is more difficult to localize a node in underwater environment compared to terrestrial. The global positioning system (GPS) signals can not travel underwater, so in UWSNs the use of GPS service for localization is not feasible. The sensor nodes deployed in underwater network are greatly affected by water currents. Due to water currents the sensor nodes move freely. In order to find the accurate position of sensor nodes, we introduce an effective localization solution. An AUV-aided localization technique which helps to localize ordinary nodes with less localization error is introduced in this paper. Three mobile AUVs are introduced in proposed scheme, that act as a reference nodes. These mobile AUVs are deployed in underwater network at predefined depth. The mobile AUVs accelerate towards the surface to find their three dimensional coordinates with the help of GPS satellite and dive back to underwater network. These mobile nodes act as reference nodes and are responsible for the localization of ordinary unlocalized nodes. By exploiting spatial correlation, the ordinary nodes predict their mobility pattern. Mobile AUVs provide enough coverage to the underwater network, which results in efficient localization.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".