An Enhanced Queue Management Approach for Greedy Routing in MANETs
Bibliographic record
Abstract
Mobile Ad-hoc network (MANET) is the guiding technology of ubiquitous era and a cornerstone in the 4G communication architecture. MANET has limited resources and characterized with high mobility and frequent topology change. Routing in MANET should follow these constrains, otherwise, it can severely degrade the performance of MANET. Owing to Greedy routing (GFS) nature in MANET, a certain part of the MANET becomes congested before other parts. In such area the nodes have failed to forward packets in a timely manner or drop them, thus such nodes called Network Holes. This paper presents a simple and efficient enhanced GFS routing algorithm called Intelligent Fuzzy logic Greedy forwarding Scheme (IFGFS). IFGFS consider the De-congestion level of each mobile node as another selective metric besides distance. In order to validate our algorithm, we rely on a wide simulation experiments. The evaluation results showed the correctness of our new proposed algorithm the adaptive- congestion IFGFS which maximize the performance of the network.
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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 | 0.001 | 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 | 0.001 | 0.010 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".