Adaptive Expiration Time for Dynamic Beacon Scheduling in Vehicular Ad-Hoc Networks
Bibliographic record
Abstract
In Vehicle Ad-hoc Networks, beacon is generated periodically to provide adequate awareness of the surrounding vehicles and environment. Generating periodic beacons at the same rates for all vehicles, typically high rates for safety applications, consume sizeable resources on communication channel. This is in turn presents a challenge to a reliable and successful delivery. This problem gains a lot of attention and researchers started to come up with many fundamentally different solutions to adjust beacons rate for better scalability. However, adjusting beacon rate without a good estimate of beacon data lifetime may impact the accuracy of the awareness of the surrounding vehicles. Particularly, for the applications and protocols that require knowledge about network topology. Accordingly, we propose a new mathematical formula, Adaptive Expiry Time (AET), to determine the lifetime of beacon data. It is independent of beacon scheduling interval and based on neighbour position, speed and orientation. It has been evaluated using proposed Dynamic Beacon Scheduling (DBS) that adjusts beacon interval according vehicle speed. Furthermore, it has been compared to different approaches of expiry time, such as Constant Expiry Time (CET), Variable Expiry Time (VET).
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".