A content centric approach to dissemination of information in 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
Data dissemination in dynamic environments such as vehicular networks has been a critical challenge. One of the key characteristics of vehicular networks is the high intermittent connectivity. Recent studies have investigated and proven the feasibility of a content-centric networking paradigm for vehicular networks. Content-centric information dissemination has a potential number of applications in vehicular networking, including advertising, traffic and parking notifications and emergency announcements. It is clear and evident that knowledge about the type of content and its relevance can enhance the performance of data dissemination in VANETs. In this paper we address the problem of information dissemination in vehicular network environments and propose a model and solution based on a content-centric approach of networking. We leverage the expansion properties of interacting nodes in a cluster to be interpreted in terms of social connections among nodes and perform a selective random network coding approach. We compare the reliability performance of our method with a conventional random network coding approach and comment on the complexity of the proposed solution.
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.
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 | 0.001 |
| 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