Highway multihop broadcast protocols for vehicular networks
Bibliographic record
Abstract
IEEE 802.11p Wireless Access in the Vehicular Environment (WAVE) standard is being developed, in order to support Intelligent Transportation System (ITS) applications including safety applications. This includes data exchange between vehicles (V2V), and between vehicles and infrastructure (V2I). Safety applications, e.g., collision and other safety warnings, rely on broadcast communication. Unfortunately, 802.11p does not allow mechanisms such as sending RTS/CTS and acknowledgements for broadcast communication. Therefore, several collisions can be caused by hidden nodes/vehicles. Moreover, the possibility of using the binary exponential back-off technique to reduce congestion is not supported due to the lack of acknowledgements. In this paper, we propose a new 802.11 based Vehicular Multi-hop Broadcast protocol, called Highway Multihop Broadcast (HMB) that addresses the broadcast storm, hidden node, and reliability problems of multi-hop broadcast in VANET. HMB selects the farthest vehicle, with the least speed deviation with respect to the source, to forward and acknowledge broadcast frames. Simulation results show that HMB has a very high success rate in delivering safety messages, and efficient channel utilization when compared with existing broadcast based protocols.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".