A Mobile Node Assisted Localization System for Wireless Sensor Networks
Bibliographic record
Abstract
Wireless sensor network (WSN), consisting of several sensor nodes, is one of the most promising technologies emerged in the past decade. The positioning system for WSN is particularly meaningful and widely used in the military surveillance, air-sea rescue, traffic monitoring, and etc. However, the traditional positioning system always suffers from deployment and maintenance of anchors. In this paper, we propose a positioning system employing a Raspberry Pi platform attached to a DJI drone as a mobile anchor. The DJI drone can serve as multiple virtual anchors by moving and broadcasting its location information periodically. Thus, it is possible to localize sensor node by itself when the sensor node collects the drone's position. A Gauss-Newton method is applied to improve the accuracy of the proposed positioning system. We also elaborate the adaption of the Gauss-Newton method with the geodetic coordinates. The goal of the proposed positioning system is to achieve higher accuracy and higher coverage at lower cost.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".