Performance Evaluation of Routing Protocols for Vehicle-to-Vehicle Communication in Urban VANETs Using Simulation Based Metrics
Bibliographic record
Abstract
Transportation intelligent interface requires Vehicular Ad Hoc Networks that enable management of traffic, provision of road safety and traffic optimization in cities. Dynamic urban landscapes have challenged current routing protocols including AODV, DSR and OLSR with the speed of node movement and fluctuating traffic density and connectivity asymmetry. These restrictions may result in routing lengthiness, additional end-to-end latency, increased control wastage and axiomatic packet transfer. As a way of eliminating these obstacles, the following paper introduces a simplified VANET routing model combining smarter node prioritization, smart path selection and smart lossless routing to guarantee the forwarding of packets. The most dynamic nodes in the model occur relative to the throughput, connectivity and the likelihood of the loss of packets and other related matters and achieves the best possible paths with few hops, latency and controlling traffic and maximum reliability. The strategy exploits the strengths of the high throughput nodes as relays in the backbone and avoids the low throughput nodes to enhance easier distribution of traffic and low bottlenecks. Performance is assessed on the simulation of an urban VANET on a snapshot basis and finally, measurements of performance are the path length, end-to-end delay, throughput, routing overhead, and the loss of packets. Visualizations such as network graphs, routing paths, and intensity heatmaps of coverage as well as the level of throughput of individual nodes all reveal the general behaviour of the network and individual nodes. The results have revealed that the model is superior to the conventional reactive and proactive model in that it offers shorter route, latency and larger throughput and lessened overhead and augmented reliability. The proposed routing model is a robust and adaptable solution to dynamic urban VANET settings that may have desirable values to both useful and scaled motives to next-generation vehicle to vehicle communication networks.
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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 | 0.003 | 0.006 |
| 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 | 0.001 | 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".