Enabling relay-aided IP communications in 802.11p/WAVE Networks
Bibliographic record
Abstract
Vehicular communications networks and the 802.11p/WAVE technology have become a fundamental platform for providing real-time access to safety and entertainment information in vehicular scenarios. In particular, IP-based infotainment applications are key to leverage the deployment costs of the 802.11p/WAVE network. However, the operation of IP in the standard 802.11p/WAVE and its throughput performance are still unclear, as the standard recommendations for the operation of IPv6 over WAVE are rather minimal. Consequently, this paper focuses on the provision of infrastructure-based IP communications in 802.11p/WAVE networks, and proposes the Vehicular IP in WAVE (VIP-WAVE) framework, which defines a new per-user assignment and mobility management of IPv6 addresses supported by Proxy Mobile IPv6 over WAVE, and exploits relay-aided communications to improve the network performance along roads with different levels of infrastructure presence. Extensive simulations are carried out to demonstrate the effectiveness of the proposed framework to enhance the performance of IP applications in the vehicular network.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".