Request-Adaptive Packet Dissemination for Context-Aware Services in Vehicular Networks
Bibliographic record
Abstract
Many applications in vehicular networks are context-aware in that the observations of sensing nodes, potentially vehicles, at a target location should be made available to the requesting node possibly at a different location. In order to provision such applications, two phases of packet routing between the requesting node and the target location and packet dissemination within the target location need to be implemented. In this paper, we focus on the second phase and propose an efficient reliable packet dissemination mechanism, Request-adaptive Packet Dissemination Mechanism (RPDM), in target location. RPDM allows for different applications to generate request packets based on their exclusive observation needs and delay requirements and adapts the dissemination mechanism in target location to specific needs of the request packet received. Besides, RPDM also takes the intrinsic characteristics of vehicular environments into account to make sure roadmap and connectivity constraints are considered and broadcast storm is prevented. Finally, RPDM is compared with a best-known state-of-the-art data dissemination mechanism, COR, and the results show that RPDM outperforms COR in terms of both resolution time and packet overhead traffic.
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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 | 0.002 | 0.005 |
| 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) | 0.001 | 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".