An Intersection Dynamic VANET Routing Protocol for a Grid Scenario
Bibliographic record
Abstract
Vehicular Ad-Hoc NETworks (VANETs) have received considerable attention in recent years, due to its unique characteristics, which are different from Mobile Ad-Hoc NETworks (MANETs), such as rapid topology change, frequent link failure, and high vehicle mobility. The main drawback of VANETs network is the network instability, which yields to reduce the network efficiency. This paper proposes a novel Intersection Dynamic VANET Routing (IDVR) protocol, which aims to increase the route stability, average throughput, and reduce end-to-end delay in a grid topology. We used a centralized Software Defined Network (SDN) to gather a real-time traffic information and provide the Intersection Cluster Head (ICH) a Set of Candidate Shortest Routes (SCSR). At the Intersection, An ICH algorithm is proposed based on the maximum Life Time (LT), the LT is the time that each vehicle requires till it leaves the cluster. The IDVR protocol selects the optimal route based on its current location, destination location, and the maximum of the minimum average throughput among the SCSR. We used SUMO traffic generator simulator and MATLAB to evaluate the performance of our proposed protocol. Our proposed protocol outperforms many protocols mentioned in the literature, such as IRTIV, VDLA, and GPCR, in terms of end-to-end delay and throughput.
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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 | 0.000 |
| 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".