Adaptive Routing Protocol in Mobile Ad-Hoc Networks Using Genetic Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
Mobile Adhoc Network (MANET) is a wireless network in which data is transferred in a forwarding direction from the source node to the destination node via multiple intermediate nodes. Packets collision is considered one of the most crucial limitations in MANETs because the nodes in the network move in random directions at a random velocity which increases the probability of collision and this will harm the throughput, the routing overhead, and the end-to-end delay. Also, frequent node mobility leads to a topological change and link instability and this reduces the data delivery rate. Because of limited available paths to the destination node or having a high traffic load, the possibility of traffic congestion augments at the intermediate nodes which in turn affects the packet delivery, particularly with real-time applications in MANETs. In this paper, we propose an adaptive routing protocol based on a bio-inspired genetic algorithm (GA). We optimize the multiple paths returned by the AOMDV mechanism (AOMDV-FG) to select the best path to the destination. The route with the highest fitness value is considered the most optimum route. Lastly, we compare our proposed protocol with AOMDV-TA and EHO-AOMDV. We have used routing overhead, end-to-end delay, throughput, energy consumption, and packet delivery ratio as key metrics for the performance evaluation of our proposed model.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| 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 it