Deployment Strategy for Multiple Controllers Based on the Aviation On-Board Software-Defined Data Link Network
Bibliographic record
Abstract
In light of the escalating demand and intricacy of services in contemporary terrestrial, maritime, and aerial combat operations, there is a compelling need for enhanced service quality and efficiency in airborne cluster communication networks. Software-Defined Networking (SDN) proffers a viable solution for the multifaceted task of cooperative communication transmission and management across different operational domains within complex combat contexts, due to its intrinsic ability to flexibly allocate and centrally administer network resources. This study pivots around the optimization of SDN controller deployment within airborne data link clusters. A collaborative multi-controller architecture predicated on airborne data link clusters is thus proposed. Within this architectural framework, the controller deployment issue is reframed as a two-fold problem: subdomain partitioning and central interaction node selection. We advocate a subdomain segmentation approach grounded in node value ranking (NDVR) and a central interaction node selection methodology predicated on an enhanced Artificial Fish Swarm Algorithm (AFSA). The advanced NDVR-AFSA (Node value ranking-Improved artificial fish swarm algorithm) algorithm makes use of a chaos algorithm for population initialization, boosting population diversity and circumventing premature algorithm convergence. By the integration of adaptive strategies and incorporation of the genetic algorithm’s crossover and mutation operations, the algorithm’s search range adaptability is enhanced, thereby increasing the possibility of obtaining globally optimal solutions, while concurrently augmenting cluster reliability. The simulation results verify the advantages of the NDVR-IAFSA algorithm, achieve a better load balancing effect, improve the reliability of aviation data link cluster, and significantly reduce the average propagation delay and disconnection rate, respectively, by 12.8% and 11.7%. This shows that the optimization scheme has important significance in practical application, and can meet the high requirements of modern sea, land, and air operations to aviation airborne communication networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".