RMobiGen: a trace generation, visualization, and performance analysis tool for random mobility models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Building a valid, credible, and appropriately detailed simulation model is crucial for conducting accurate and meaningful simulation study in mobile computing. In a recent survey conducted on the papers published in the proceedings of ACM MobiHoc symposium between 2000 and 2005, it is observed that the credibility of the simulation results has decreased while the use of simulation has increased. Part of this credibility crisis is related to the simulation of mobility of the nodes in the system. It is not surprising to see that mobility has such a fundamental influence on the behavior of mobile systems. Therefore, a clear knowledge about mobility used in the system is not only helpful but also essential for the understanding and interpretation of the system behavior under study.Proper knowledge about the mobility of the nodes in the system can be better obtained by having a tool or a component that is independent and interactive to specify, visualize, analyze, and then generate mobility traces for the simulation. This paper present such a mobility generator software tool called RMobiGen that we developed. We conducted extensive simulation experiments on RMobiGen to analyze the random mobility models for movement, coverage, and connectivity analysis of the nodes.
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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 | 0.000 | 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 | 0.001 |
| 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 it