Modeling and simulation of vehicular networks
Bibliographic record
Abstract
Vehicular networks are characterized by highly dynamic network topologies, frequent network fragmentations and the fact that movements of vehicles are constrained to pre-defined roadways. Researchers have devoted considerable efforts to the development of innovative protocols and mechanisms to address the demanding quality of service requirements of various vehicular applications, taking into account of these special characteristics. Even though field testing yields more realistic results, it potentially involves more hazards and can be prohibitively expensive when done at scale. Hence, simulation has been the tool of choice for evaluating the performance of vehicular networking protocols and mechanisms. For simulating a wireless communication scenario in a vehicular networking environment, both the mobility of vehicles and the wireless communications between them should be modeled using appropriate traffic and network simulators, respectively. A conversion tool needs to be used to convert the outputs of traffic simulators to trace-files readable by network simulators. Note that in this case the generation of the trace-files takes place before the network simulation begins. However, for some vehicular applications such as safety or traffic applications, the movements of vehicles are affected by the received packets. So, both traffic and network simulators are expected to be running simultaneously and exchanging data. In this paper, we survey a comprehensive set of both traffic and network simulators as well as possible conversion tools and integration alternatives. We believe that this paper helps the researchers new to the field select appropriate vehicular network platforms and provide them with helpful insights as they run their first vehicular simulations.
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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".