An Adaptive Frame Length Aggregation Scheme in Vehicular Delay-Tolerant Networks
Bibliographic record
Abstract
Vehicular delay-tolerant networks (VDTN) experience high-speed mobility and volatile topology on a large scale. Therefore, VDTN may not guarantee end-to-end connections. This gives the MAC layer the opportunity to adapt its transmission strategy to the current unstable wireless connections in order to improve transmission efficiency. We propose an adaptive frame length aggregation scheme in VDTN in order to improve transmission efficiency and increase data throughput. In our scheme, suitable aggregation frame lengths are calculated according to the current wireless status, and are applied in the MAC layer at the initiation of the data transmissions. We analyze and apply our adaptive frame length aggregation strategy to the current frame aggregation schemes in 802.11. Our simulations demonstrate improved results in data throughput, retransmissions, and transmission efficiency, compared to non-adaptive aggregation schemes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".