Routing in unmanned aerial ad hoc networks: A recovery strategy for Greedy geographic forwarding failure
Bibliographic record
Abstract
Unmanned Aeronautical Ad Hoc Networks (UAANETs) are a type of Mobile Ad Hoc Networks (MANETs) which are infrastructureless and self-organizing networks. The specificity of UAANETs is that they are formed by small and medium sized Unmanned Aerial Vehicles (UAVs) also known as drones. In UAANETs as well as in MANETs, geographic routing is widely used. Geographic routing relies on Greedy Forwarding (GF), also called Greedy Geographic Forwarding (GGF). GGF fails when a packet arrives at a node that has no neighbor closer to the destination than it is. The node in this situation is referred to as a void node. In this paper, we propose a strategy that salvages packets in void node situations. We thereafter append this strategy to a protocol that features GGF. Simulations in OPNET show an increase in packet delivery ratio of about 2% at virtually no additional cost.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".