Improving routing in networks of Unmanned Aerial Vehicles: Reactive‐Greedy‐Reactive
Bibliographic record
Abstract
ABSTRACT Because of their specific characteristics, Unmanned Aeronautical Ad‐hoc Networks (UAANETs) can be classified as a special kind of mobilead hocnetworks. Because of the high mobility of Unmanned Aerial Vehicles, designing a good routing protocol for UAANETs is challenging. Here, we present a new protocol called Reactive‐Greedy‐Reactive (RGR) as a promising routing protocol in high mobility and density‐variable scenarios. RGR combines features of reactive MANET routing protocols such as Ad‐hoc On‐demand Distance Vector with geographic routing protocols, exploiting the unique characteristics of UAANETs. In addition to combining reactive and geographic routing, the protocol has a number of features to further improve the overall performance. We present the rationale and design of the protocol, discuss the specific performance improvements in detail and provide extensive simulation results that demonstrate that RGR outperforms purely reactive or geographic routing protocols. The results also demonstrate the impact of the various protocol modifications. Copyright © 2012 John Wiley & Sons, Ltd.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".