Integrating Deep Learning into Location-Aided Routing (LAR) for Enhanced Mobile Ad-Hoc Networks (MANET): A Comprehensive Survey
Bibliographic record
Abstract
This research advances mobile device communication in Mobile Ad-Hoc Networks (MANETs), where devices frequently move and alter their connections.By utilizing neural networks, a form of artificial intelligence, the study captures past movement patterns to predict optimal data transmission routes, enhancing reliability and efficiency.Deep learning strategies, such as Q-learning, were employed to enable real-time adaptation of the network based on node mobility, signal strength, and environmental factors.This dynamic approach ensures the network can seamlessly handle changes and consistently route messages through the best possible paths.The integration of deep learning techniques with Location-Aided Routing (LAR) significantly improved MANET performance.Combining these advanced algorithms with LAR's geographic data framework optimized routing efficiency and adaptability in dynamic network conditions.Key Performance Indicators (KPIs), including throughput, delay, and energy efficiency, demonstrated the effectiveness of this approach under diverse conditions.The results showcase how this intelligent framework not only makes MANETs more adaptive to unpredictable changes but also fosters robust and efficient communication.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".