An Intelligent Congestion Avoidance Mechanism Based on Generalized Regression Neural Network for Heterogeneous Vehicular Networks
Why this work is in the frame
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Bibliographic record
Abstract
The information generated by safety and traffic efficiency applications needs strict communication requirements to be smoothly exchanged in intelligent transportation system. Unfortunately, data congestion is still a challenge that negatively affects network performance. In this paper, we propose an Intelligent Congestion Avoidance Mechanism (ICAM) to prevent congestion in Heterogeneous Vehicular Network (HetVNET) that adapts the Dedicated Short Range Communication (DSRC) transmission power using a Generalized Regression Neural Network (GRNN) to predict data congestion. We compare the performance of the proposed GRNN congestion prediction model to other well-known models such as Multiple Linear Regression (MLR), Support Vector Machine (SVM) for regression, Decision Tree Regression (DTR), and Multi-layer Perceptron for Regression (MLPR). Numerical results show that the proposed GRNN congestion prediction model outperforms those other models in terms of accuracy, reliability and stability. Furthermore, simulation results show a substantial network performance improvement compared to other congestion control methods in terms of packet delivery ratio, average delay, and packet loss ratio.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it