VIP-WAVE: On the Feasibility of IP Communications in 802.11p Vehicular Networks
Bibliographic record
Abstract
Vehicular communication networks, such as the 802.11p and Wireless Access in Vehicular Environments (WAVE) technologies, are becoming a fundamental platform for providing real-time access to safety and entertainment information. In particular, infotainment applications and, consequently, IP-based communications, are key to leverage market penetration and deployment costs of the 802.11p/WAVE network. However, the operation and performance of IP in 802.11p/WAVE are still unclear as the WAVE standard guidelines for being IP compliant are rather minimal. This paper studies the 802.11p/WAVE standard and its limitations for the support of infrastructure-based IP applications, and proposes the Vehicular IP in WAVE (VIP-WAVE) framework. VIP-WAVE defines the IP configuration for extended and non-extended IP services, and a mobility management scheme supported by Proxy Mobile IPv6 over WAVE. It also exploits multi-hop communications to improve the network performance along roads with different levels of infrastructure presence. Furthermore, an analytical model considering mobility, handoff delays, collisions, and channel conditions is developed for evaluating the performance of IP communications in WAVE. Extensive simulations are performed to demonstrate the accuracy of our analytical model and the effectiveness of VIP-WAVE in making feasible the deployment of IP applications in the vehicular network.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".