Relay Selection for Heterogeneous Transmission Powers in VANETs
Bibliographic record
Abstract
The main challenge in the vehicular ad hoc network vehicle-to-vehicle (V2V) multi-hop dissemination is to control the number of vehicles, that relay the broadcast message. Proper selection of relay nodes governs high delivery ratio, acceptable overall end-to-end delay, and efficient bandwidth usage. To date, several protocols have been proposed to identify appropriate relay vehicles. However, such approaches neglect the fact that vehicle transmission ranges are typically heterogeneous due to different transmission power values or dynamic adjustment of power to alleviate congestion and/or control energy consumption. In this paper, we introduce area-based dissemination protocols that work in heterogeneous transmission powers. The transmissions between relay vehicles are ordered in way that ensures that the node with high potential new coverage area transmits first. This eliminates useless transmission and retransmission that could be contained by other transmission. The new potential coverage area is computed as a function of the common overlap areas. In addition, we propose more reliable approaches by relaying duplicate received message. Thus, we introduce a geometric taxonomy for all possible overlap patterns in wireless environment, which is an apparently hitherto unsolved geometrical problem. Accordingly, we deduce the criteria used to define each pattern and relevant algebraic expression to compute the potential additional coverage area.
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".