Tutorial Information-Centric Vehicular Networking: Why and Wherefores, Challenges, and Design Guidelines
Bibliographic record
Abstract
We are witnessing the development of the new era of the vehicles' evolution history: the era of connected and autonomous vehicles. In this new era, vehicles will be empowered with processing, sensing, actuators and wireless communication capabilities, which will support a wide range of vehicular applications to improving safety, efficiency and enjoyableness of transportation. In this revolution, processing and exchange of multimedia and big data will be fundamental. Therefore, content distribution is one of the critical challenges in connected cars and intelligent vehicular networks. In this tutorial, we will discuss the recent advancements and research directions towards the development of solutions for content distribution in connected cars and vehicular networks. We will highlight the current state-of-the-art and identify research opportunities that could interest researchers willing to contribute in different areas, such as vehicles' mobility characterization and modeling, modeling and performance evaluation of information-centric networking in vehicular networks, broadcast storm avoidance of Interest and Data packets, content placement and cache policies, as well as networking protocols and architectures for service-oriented information-centric multimedia content distribution for vehicular networking and connected vehicles' applications [1]-[12].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".