Efficient and Reliable Broadcast in Intervehicle Communication Networks: A Cross-Layer Approach
Bibliographic record
Abstract
Broadcast transmission is an effective way to disseminate safety-related information for cooperative driving in intervehicle communication (IVC). However, it is fraught with fundamental challenges such as message redundancy, link unreliability, hidden terminals, and broadcast storms, which greatly degrade network performance. In this paper, we introduce a cross-layer approach to design an efficient and reliable broadcast protocol for emergency message dissemination in IVC systems. We first propose a novel composite relaying metric for relay selection by jointly considering geographical locations, physical-layer channel conditions, and moving velocities of vehicles. Based on the relaying metric, a distributed relay-selection scheme is proposed to assure that a unique relay is selected to reliably forward the emergency message in the desired propagation direction. We further apply IEEE 802.11e enhanced distributed coordination access (EDCA) medium-access control (MAC) to guarantee quality-of-service (QoS) provisioning to safety-related services. In addition, an analytical model is developed to study the performance of the proposed cross-layer broadcast protocol (CLBP) in terms of the relay-selection delay and the emergency message access delay. Network Simulator (NS-2) simulation results are given to validate our analysis. It is shown that the CLBP not only can minimize the broadcast message redundancy but can quickly and reliably deliver emergency messages in IVC as well.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".