A Cross Layer Broadcast Protocol for Multihop Emergency Message Dissemination in Inter-Vehicle Communication
Bibliographic record
Abstract
In order to achieve cooperative driving in vehicular ad hoc networks (VANET), broadcast transmission is usually used for disseminating safety-related information among vehicles. Nevertheless, broadcast over multihop wireless networks poses many challenges due to link unreliability, hidden terminal, message redundancy, and broadcast storm, etc., which greatly degrade the network performance. In this paper, we propose a cross layer broadcast protocol (CLBP) for multihop emergency message dissemination in inter-vehicle communication systems. We first design a novel composite relaying metric for relaying node selection, by jointly considering the geographical locations, physical layer channel conditions, moving velocities of vehicles. Based on the designed metric, we then propose a distributed relay selection scheme to guarantee that a unique relay is selected to reliably forward the emergency message in the desired propagation direction.We further apply IEEE802.11e EDCA to guarantee QoS performance of safety related services. Finally, simulation results are given to demonstrate that CLBP can not only minimize the broadcast message redundancy, but also quickly and reliably disseminate emergency messages in a VANET.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".