VANETs Routing Optimization: Combining Message Priority, Free Nodes, and Mobility Direction
Bibliographic record
Abstract
Vehicular Ad Hoc Networks (VANETs) face challenges due to high vehicle mobility, which leads to frequent changes in network topology and unstable links. To tackle this, we propose a novel routing strategy called MPFD, which enhances message delivery rates and reduces End-to-End (E2E) delay. MPFD operates in two phases. The first phase, the message management phase, prioritizes messages based on urgency and controls message propagation by considering hop count (H) and Time-To-Live (TTL), ensuring only critical messages are forwarded. The second phase, the relay node selection phase, uses free nodes to identify the best relay for forwarding messages. Candidate nodes are evaluated based on community association, movement direction, and proximity to the destination. Simulations show that the MPFD strategy significantly improves message delivery speed, reduces overhead and E2E delay, and enhances the overall message delivery ratio in VANETs.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".