A contention-free broadcast protocol for periodic safety messages in vehicular Ad-hoc networks
Bibliographic record
Abstract
Ad-hoc multi-hop broadcast protocols are usually used in vehicular networks to provide safety services. However, these protocols face several issues, namely broadcast storms, hidden nodes, and message delivery failures, that prevent safety applications from guaranteeing their required high message delivery ratio and low delays. In this paper, we tackle these issues using a novel cluster-based contention-free broadcast protocol. Particularly, we propose an efficient time slot reservation protocol, centralized in stable cluster heads that continuously adapts to vehicles dynamics. Thus, using a centralized protocol, we ensure an efficient utilization of the time slots for the exact number of active vehicles including hidden nodes; our protocol also ensures a bounded delay for safety applications to access communication channel. We reduce the overhead of our reservation protocol using a directed broadcast propagation and a single reservation request for a periodic medium access during a vehicle's cluster session. During the recurrent service interval, a contention-based period follows the efficiently-used contention free period; it is dynamically adjusted to improve throughput-sensitive non-safety applications. Extensive simulation results show that the proposed scheme can significantly improve the periodic safety application performance in terms of safety message delivery ratio and delay.
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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) | 0.000 | 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".