On improving delay performance of IEEE 802.11p vehicular safety communication
Bibliographic record
Abstract
In this work, we present the design of an effi cient Deterministic medium Access (DA) for Dedicated Short-Range Communication (DSRC) vehicular safety communication over IEEE 802.11p, called Vehicular DA (VDA). VDA supports two types of safety services (emergency and routine safety messages) with different priorities and strict requirements on delay, especially for emergency safety messages. VDA processes both types of safety messages to maintain a balance between two confl icting requirements: reducing chances of packets collisions and lowering the transmission delay. VDA allows vehicles to access the wireless medium at selected times with a lower contention than it would otherwise be possible within two-hop neighbourhood with the classical 802.11p EDCA or DCF schemes. Besides, we propose an improvement of VDA called Dynamic VDA opportunities Re-assignment (DVR) to avoid network performance degradation caused by interference outside the two-hops. Particularly, our scheme provides an effi cient adaptive adjustment of the Contention Free Period (CFP) duration to establish a priority between emergency and routine messages. Simulations show that the VDA scheme, used with 802.11p, clearly outperforms 802.11p alone in high-offered load conditions while bounding the transmission delay of safety messages. Furthermore, beyond two-hops, DVR is able to effi ciently tackle the interference phenomenon by reducing losses and delays of safety applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".