Design Guidelines for Information-Centric Connected and Autonomous Vehicles
Bibliographic record
Abstract
The ubiquitous connectivity between vehicles and smart transportation infrastructure will facilitate the information flow necessary for empowering smart service for efficient, safe, and eco-friendly transportation of people and goods. Although vehicular communication technologies is quickly advancing toward ubiquitously connected cars, the design of networking protocols for such vehicles is still in its infancy, facing multiple daunting challenges. In this article, we advocate for the use of the information-centric networking (ICN) paradigm for the full realization of connected cars, that is, the integration of both in-car computing systems and the car-to-digital world. We discuss the emerging challenges for the design of ICN-based protocols for connected cars, and we formally put the current debate into perspective regarding which transmission mode (broadcast or unicast) should prevail for the design of such protocols. Concerning this debate, we propose to classify recent and representative protocols in broadcast and unicast-oriented solutions, and we highlight the trade-offs of each approach. Based on a critical qualitative evaluation, we advocate that adaptive and hybrid approaches would perform better for information- centric connected cars. Hence, we provide a guideline for the further design of ICN-based protocols for connected cars. Finally, we discuss some future research directions that might be considered to advance the process of content distribution in connected cars.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".