A Novel Location-Based Content Distribution Protocol for Vehicular Named-Data Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The peculiar characteristics of vehicular networks (e.g., high vehicular mobility, poor wireless link quality and short-lived and intermittent connectivity among vehicles) challenge host-centric content search and distribution in vehicular networking applications. In this regard, recent studies have proposed information-centric protocols to improve content distribution in vehicular networks. However, they are still severely impaired by the highly dynamic nature of vehicular network topologies and the broadcast storm problem due to uncontrolled Interest packet flooding for content discovery. In this paper, we tackle the broadcast storm problem of Interest packet transmissions for content discovery in vehicular named-data networks. We propose the location-based content distribution protocol (LOCOS) for oriented Interest packet transmissions towards the proximity area of a recently discovered content source vehicle. The LOCOS protocol leverages the recently discovered location of a vehicle content source to controlled transmit Interest packets to the area where the content source is located. Simulation results show that the LOCOS protocol improves content delivery rate in 10% and 28% when compared with related work, while it reduces the content delivery delay in 80%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it