A joint 3D localization and synchronization solution for Wireless Sensor Networks using UAV
Bibliographic record
Abstract
Localization and synchronization are fundamental services in Wireless Sensor Networks (WSNs), since it is often required to know the position and the global time of sensor nodes to relate a given event detection to a specific location and time. However, the localization and synchronization tasks are often performed after the sensor nodes' deployment. Since manual configuration of sensor nodes is an impractical activity, it is necessary to rely on specialized algorithms to solve the localization and synchronization problems. With this in mind, in this work we propose a joint solution for the 3D localization and time synchronization in WSNs using an unmanned aerial vehicle (UAV). A UAV equipped with a GPS flies over the sensor field area broadcasting its geographical position. Therefore, sensor nodes are able to estimate their own geographical position and global time without the need of equipping them with a GPS device. By means of simulations, we show that our proposed joint solution leads to smaller time-synchronization and localization errors when compared to existing solutions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".