Age of Information Minimization for UAV-Assisted Internet of Things Networks: A Safe Actor-Critic With Policy Distillation Approach
Why this work is in the frame
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Bibliographic record
Abstract
Thanks to smart manufacturing and artificial intelligence technologies, unmanned aerial vehicles (UAVs) are envisioned to play a critical role in future Internet of things (IoT) networks to execute data collection tasks. In this article, we leverage age of information (AoI) to measure the freshness of data packets received by the UAV from IoT sensors. Considering the heterogeneity of IoT devices, we aim to minimize the weighted sum AoI by jointly optimizing the UAV's trajectory and IoT devices association in UAV-assisted IoT networks, where the UAV's cumulative propulsion energy cost is limited by the battery capacity. Since the optimization object is confined by a set of short-term constraints and a long-term constraint, this problem is modeled as a constrained Markov decision process (CMDP). We leverage safe actor-critic (Safe-AC) to solve the CMDP. To satisfy the mixed constraints, the safe policy set of Safe-AC is induced by a Lyapunov function, thereafter, a policy distillation technology is leveraged to search the optimal policy. Experimental results indicate that our proposed scheme can strictly satisfy the propulsion energy cost budget requirement at the expense of around 2% loss of the reward compared to baseline methods.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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