A Probability-Based Multimetric Routing Protocol for Vehicular Ad Hoc Networks in Urban Scenarios
Bibliographic record
Abstract
Vehicular Ad hoc Networks have received considerable attention in recent years and are considered as one of the most promising ad-hoc network technologies for intelligent transport systems. Vehicular Ad hoc Networks have special requirements and unique characteristics (e.g., special mobility patterns, short life links, rapid topology changes) which make the design of suitable routing protocols, a challenge. Consequently, an efficient routing protocol that fits with VANETs' requirements and characteristics is a crucial task to obtain a good performance in terms of average percentage of packet losses and average end-to-end packet delay. To attain this goal, we propose a novel probabilistic multimetric routing protocol (ProMRP) that is specially designed for VANETs. ProMRP estimates the probability for each neighbor of the node currently carrying the packet, to successfully deliver a packet to destination. This probability is computed based on four designed metrics: distance to destination, node's position, available bandwidth and nodes' density. Furthermore, an improved version of ProMRP called EProMRP is also proposed. EProMRP includes an algorithm that accurately estimates the current position of nodes in the moment of sending the packet instead of using the last updated position obtained from the previous beacon message. Simulations are carried out in a realistic urban scenario using OMNeT++/VEINS/SUMO, including real maps from the OpenStreetMaps platform. Simulation results show a better performance of ProMRP and EProMRP compared to recent similar proposals found in the literature in terms of packet losses and end-to-end packet delay, for different vehicles' densities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".