Information-Centric Strategies for Content Delivery in Intelligent Vehicular 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
Efficient content delivery in vehicular networks will play a fundamental role in empowering envisioned vehicular and smart transportation applications. However, the peculiar characteristics of vehicular networks (e.g., high mobility, dynamic network topologies, and intermittent connectivity), as well as the string QoS requirements of the applications, challenge the design of efficient solutions for content delivery in these environments. In this paper, we discuss the recent trend of applying the information-centric networking (ICN) paradigm for content delivery in vehicular networks. By doing so, we highlight the potentials of this novel paradigm and how it can deal with the severe challenges of vehicular networks. Besides, we point out the limitations of current ICN implementations when applied in vehicular networks. Furthermore, we discuss the new challenges that need be tackled in ICN-based vehicular networks, the proposed solutions encountered in the literature and, based on that, we provide some guidelines for the design of new solutions and some future research directions.
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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 | 0.001 |
| Open science | 0.000 | 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