An enhanced Gauss-Markov mobility model for simulations of unmanned aerial ad hoc networks
Bibliographic record
Abstract
Routing protocols are designed assuming certain application-specific network characteristics. In order for a routing protocol to be effective and reliable it needs to be evaluated with a realistic mobility model. The Random Waypoint mobility model, widely used, allows node to stop suddenly and turn sharply, and therefore fails to capture the movement pattern of actual airborne vehicles. In this paper we propose the Enhanced Gauss-Markov (EGM) mobility model, a realistic model for networks of UAVs (UAANETs) based on the Gauss-Markov (GM) mobility model. EGM features mechanisms to eliminate/limit sudden stops and sharp turns within the simulation region. The model, unlike others, also deals explicitly with ensuring smooth trajectories at the boundaries. Simulations in OPNET show that EGM, compared to RWP, results in many more network partitions. This then suggests that network partitioning is a significant issue that ought to be dealt with in the protocol design for UAANETs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".