Moving Aerial Anchors Assisted Network Localization
Bibliographic record
Abstract
To provide effective wireless connectivity, the use of aerial vehicles has been proposed as a promising solution in a wide variety of applications. In some of these applications, however, to achieve desirable performance, knowing the location of users may be a necessity. One promising approach for solving the problem of obtaining user locations is through the locations of some nodes (called anchors) and measuring the distance between connected nodes in the network. In this paper, we propose prominent techniques to improve the performance of the localization using multiple moving aerial anchors (MAA). In using MAAs as anchor nodes, the anchor nodes in our scenario have movement capability. This provides us with more flexibility to enhance the accuracy of the localization with taking into consideration the power consumption of MAAs. In fact, we propose an MAA placement method by which each user can be connected to at least one MAA while MAAs communicate with as small as possible power consumption. Then, we propose a path planning for MAAs by which distances between users and MAAs can be measured in order to estimate the locations of users. Our simulation results show that our proposed localization scheme can provide highly accurate location estimations.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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".