Deep Reinforcement Learning-based Mobile Ad Hoc Network for Secure and Dynamic Routing through Cryptographic Assurance
Bibliographic record
Abstract
In Mobile Ad Hoc Networks (MANETs), route discovery and maintenance are costly in terms of network overhead, which degrades performance, shortens network lifespan, and results in network partitioning. The extremely dynamic nature of node movement and topological changes requires the deployment of clever, adaptive routing schemes that can deliver data packets to their destinations efficiently. In such scenarios, route paths are often changed due to changes in link connectivity, resulting in reliability and stability of data transfer as a critical problem. This research deals with these problems by utilizing DRL-MANET — a Deep Reinforcement Learning-Based Routing protocol that combines route stability metrics and security-aware decision-making. DRL-MANET applies deep Q-learning to dynamically acquire the best routing routes according to the current network state, residual energy level, trust values, and link quality, and integrates anomaly detection to prevent malicious nodes. The main goal of this paper is to design and compare a DRL-based routing approach to improve route stability, packet delivery ratio, delay reduction, and security in MANET networks. The suggested framework is able to prove its worth in reaching effective, secure, and adaptive routing, providing a smooth solution for large and dynamic MANET deployments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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".