Adaptive Content Forwarding Mechanism for Platoon based Vehicular Named Data Networks
Bibliographic record
Abstract
Vehicular networking systems rely on Internet Protocol to exchange information among vehicles. With the increasing number of vehicles, the communication overhead has increased significantly a nd h as b ecome m ore c ontent centric. To resolve this problem, the Named data networking-based communication model has been used. This communication is completely based upon the content rather than the location and provides better network coverage comparatively. The vehicles used for communication purposes in a network are moving in some specific p atterns, b ased o n h aving t he s ame destination, with the same speed parameters etc. These vehicles which have common interests form a platoon. This vehicular platoon helps in various fields such as safe driving, energy efficiency and road safety. This paper provides a scheme for the applicability of NDN to the vehicular platoon. Special design features are proposed for communication purposes in V-NDN-based vehicular platoons. The backbone platoon network is used for data dissemination between the vehicles on the highway. To check the efficiency of the proposed scheme, extensive simulations have been performed on the ndnSim simulator. More precisely, different scenarios have been used and analyzed their efficiency i n t erms o f d elay and throughput.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".