Density-Based Clustering and Performance Enhancement of Aeronautical Ad Hoc Networks
Bibliographic record
Abstract
In-Flight Entertainment and Connectivity (IFEC) is becoming a key trend and an essential need. A grand challenge is to provide in-flight connectivity in high altitudes, and particularly in isolated locations, such as the oceans, where establishing an air-to-ground link is not possible. Aeronautical Ad-Hoc Networking (AANET) intends to cope with this challenge by forming a network of airplanes having air-to-air (A2A) connections. However, the dynamic nature of such a network is likely to lead to unstable connections. The primary cause of the majority of these stability issues is known to be poor clustering of aircrafts. Consequently, concentrating on aircraft clustering and making them more stable can improve connection. This paper aims to unveil the benefits of density-based clustering to improve the AANET performance. To do so, the paper employs a multi-feature DBSCAN algorithm for the clustering problem that exploits several features of real flight datasets, including latitude, longitude, altitude, direction, and velocity. Instead of a typical distance metric such as Euclidean or Haversine, the technique produces a precomputed distance matrix and feeds it to DBSCAN. This method also includes a weighted scheme to reflect the relative importance of each component of the distance calculation. Simulations under OMNET++ by using real-time flight data point out that packet delivery ratio and end-to-end latency of the state of the art clustering-based AANET solutions can be improved by 40 % and 30 %, respectively. Furthermore, the proposed method achieves a 20% reduction in cluster changes and the number of clusters.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".