Adaptive routing in mobile ad hoc networks based on decision aid approach
Bibliographic record
Abstract
In this paper, we propose a new approach to the problem of adaptive routing in small scale Mobile Ad Hoc Networks (MANET). The proposed approach focuses on leveraging the already existing routing algorithms in real-time through switching from one routing algorithm to another as the conditions change. The contribution of this work is three-fold: (1) nodes use a Multiple Criteria Decision Analysis method called PROMETHEE to make local decisions, (2) the final decision is evaluated as an aggregation of all the local decisions in the MANET through weighted voting, and (3) the network-wide decision process is carried out through a hybrid method that aims to benefit from the advantages of centralized and distributed methods, while limiting their disadvantages. The analyses based on the results extracted from simulations underline that the proposed approach produces stable configurations with minimal interruptions. Furthermore, the completion of global adaptation cycles are performed an order of magnitude faster in comparison to the results outlined in the previous work. These results indicate that the proposed solution is a promising alternative to the existing solutions, and forms a strong basis for further research on larger scale MANETs.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".