A Multimetric Predictive ANN-Based Routing Protocol for Vehicular Ad Hoc Networks
Bibliographic record
Abstract
Vehicular networks support intelligent transportation system (ITS) to improve drivers' safety and traffic efficiency on the road by exchanging traffic-related information between vehicles and also between vehicles and infrastructure. Routing protocols that are designed for vehicular networks should be flexible and able to adapt to the inherent dynamic network characteristics of these kind of networks. Therefore, there is a need to have effective vehicular communications, not only to make mobility more efficient but also to reduce collateral issues such as pollution and health problems. Nowadays, the use of machine learning (ML) algorithms in wireless networks are on the rise, including vehicle networks that can benefit from possible data-driven predictions. This work aims to contribute to the design of a smart ML-based routing protocol for vehicular ad hoc networks (VANETs) used to report traffic-related messages in urban environments. We propose a new ML-based forwarding algorithm to be used by the current vehicle holding a given packet to predict which vehicle within its transmission range is the best next-hop to forward that packet towards its destination. Our algorithm is based on a neural network designed from a dataset that contains data records that are captured during simulated urban scenarios. Simulation results show how our ML-based proposal improves the performance of our multimetric routing protocol for VANETs in urban scenarios in terms of packet delivery probability. The performance evaluation of MPANN shows packet losses lower than 20% (and average packet delays below 0.04 ms) for different vehicles' densities, in completely new scenarios but of similar complexity than the Barcelona scenario used to train the model. Even for much more complex scenarios (with narrow curvy streets), our proposal is able to reduce the packet losses in 20% with respect to the multimetric routing protocol as well as the average packet delays in 0.04 ms.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".