Minimizing AoI under Covertness Constraints in Internet of Things Networks
Bibliographic record
Abstract
Covertly collecting the latest status information is great crucial for the controller to make decision in Internet of Things (IoT) networks. In this paper, age of information (AoI) is leveraged to evaluate the information freshness. To improve the information freshness, packet re-transmission is exploited at the transmitter, while the re-transmission increases the probability that the transmission behavior is being detected by the adversaries. Hence, we propose a time constrained re-transmission strategy to make a trade-off between the AoI and transmission covertness. Specifically, the maximum allowable re-transmission times have different effects on the AoI and transmission covertness. We formulate an optimization problem to minimize AoI under the constraint of system covertness. The closed-form expression of the average AoI and the expected probability of error detection are derived. Then, the number of maximum re-transmission time slots is optimized to minimize the average AoI. Finally, numerical results demonstrate that the optimal choice of the re-transmission times is the upper bound value of the consecutive transmission time slots that satisfies the covertness constraint. Proposed retransmission strategy can guarantee the information freshness under a given covertness constraint.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".