Age-of-Information Minimization for UAV-Based Multi-View Sensing and Communication
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Bibliographic record
Abstract
Due to flexible deployment and controllable mobility, unmanned aerial vehicles (UAVs) have great potential for supporting many time-critical sensing applications. In this article, we investigate UAV-based wireless sensing and communication in which one UAV with an onboard camera sensor senses ground targets from multiple different views and transmits the sensing data to a remote ground controller (GC). With the objective of improving the freshness of the information received at the GC while ensuring the sensing quality, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MUlti-view SensIng and Communication (MUSIC)</i> framework and jointly optimize the parameters in the framework including the target visiting sequence, the number of sensing, UAV trajectory, service time and transmit power. To solve the corresponding mixed-integer non-convex problem, we propose a two-stage approach. Specifically, we first determine the target visiting sequence by considering a specific case, i.e., UAV senses each target only once, through the quadratic penalty (QP) and successive convex approximation (SCA) methods. Based on the obtained visiting sequence, we minimize the average peak age-of-information (PAoI) of all targets by jointly optimizing the variables contained in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MUSIC</i> framework via the SCA and exhaustion methods. Simulation results demonstrate that the proposed joint optimization approach outperforms the benchmark schemes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it