On the statistical behaviors of network-level features within MANETs
Bibliographic record
Abstract
Event-based simulation has become a primary means of pursuing mobile ad hoc network (MANET) research. The stochastic nature of MANETs has been well studied with respect to mobility models, but less work has looked at the statistical behaviors of network layer features, (e.g., PDR, delay, hops and routing overhead). Fundamentally, issues such as “When do start up transients end?” and “Do all Monte- Carlo runs indeed arrive at the same steady-state distributions?” have not been well explored. This work explores these issues through using the DYMO routing protocol and the OMNeT++ simulation framework as exemplars. By applying distribution free Kolmogorov-Smirnov goodness-of-fit tests it is shown that, for network-layer features: a) MANET start-up transients can persist far longer than previously reported, b) transient durations can vary significantly from feature to feature and with varying node velocities, and c) Monte-Carlo runs of a given MANET scenario can produce distinct behavioral modes. It is then discussed whether these issues are likely inherent to MANETs and their routing protocols or an artifact of OMNeT++.
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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 | 0.001 | 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".