MoMAC: Mobility-Aware and Collision-Avoidance MAC for Safety Applications in VANETs
Bibliographic record
Abstract
Time-division multiple access (TDMA) based medium access control (MAC) protocol provides a promising solution to well support delay-sensitive safety applications in vehicular ad hoc networks, since a time-slotted access scheme ensures the transmission within the ultra-low delays. However, due to the varying vehicle mobility, existing TDMA-based MAC protocols may result in collisions of slot assignment when multiple sets of vehicles move together. To avoid slot-assignment collisions, in this paper, we propose a mobility-aware TDMA MAC, named as MoMAC, which can assign every vehicle a time slot according to the underlying road topology and lane distribution on roads with the consideration of vehicles' mobilities. In MoMAC, different lanes on the same road segment and different road segments at intersections are associated with disjoint time slot sets. In addition, each vehicle broadcasts safety messages together with the time slot occupying information of neighboring vehicles; by updating time slot occupying information of two-hop neighbors (obtained indirectly from one-hop neighbors), vehicles can detect time slot collisions and access a vacant time slot in a fully distributed way. We demonstrate the efficiency of MoMAC through theoretical analysis and extensive simulations; compared with state-of-the-art TDMA MACs, the transmission collisions can be reduced by 59.2%, and the rate of safety message transmissions/receptions can be greatly enhanced.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".