Generation and analysis of realistic mobility models for mobile ad hoc networks.
Bibliographic record
Abstract
Simulation modeling is an integral part of conducting research in communication networks and distributed systems. In systems involving mobile nodes, accurate modeling of mobility has primary importance. Mobility has a fundamental influence on the behavior and performance of the system. However, only few mobility models have been used in nearly all simulations in the past. These models are simple and highly random. As a result, the simulation studies based on these random mobility models have been heavily criticized for their credibility. We feel that availability of a software tool with the following capability, at least in part, would alleviate this crisis. The software must facilitate researchers to: (i) model a wide range of mobility with varying degrees of realism (ii) analyze the modeled mobility visually and statistically and (iii) transport the mobility trace in a format that can be used in most widely used simulators. The development of a software tool with the above mentioned capabilities is the main contribution of this thesis. In this thesis, after presenting a comprehensive survey on realistic mobility models, we present a realistic mobility generator software called RLMobiGen that can be used to specify, generate, analyze, and then export the mobility trace. The mobility trace can then be used in the simulation studies of mobile ad hoc networks. RLMobiGen is a comprehensive, highly interactive, and user friendly software. --P.iii.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".