A Hybrid Ant-Colony Routing Algorithm for Mobile Ad-Hoc Networks
Bibliographic record
Abstract
The dynamic nature of mobile ad hoc networks makes it difficult to consider a specific model for their topology which might change in a short period of time. Using the knowledge about the location of nodes, several relatively efficient position based routing algorithms have been proposed but almost all of them are sensitive to the network topology. Ant colony optimization based routing algorithms form another family of routing algorithms that usually converge to optimum routes. In our previous work we proposed POSANT, a position based ant colony routing algorithm for mobile ad-hoc networks. Although POSANT outperforms other routing algorithms in most cases, there are network topologies in which POSANT does not perform well. In this paper we introduce HybNet, a hybrid ant colony optimization based routing algorithm for mobile ad hoc networks which adapts itself to different network topologies. We carry out an empirical analysis of the performance of our algorithm and compare it with other routing algorithms. Our results show that HybNet almost always performs efficiently, even in some complex and variable network topologies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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; 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".