LearnRouter: A Reinforcement Learning-Based Routing for Opportunistic Mobile Networks Using Multi-Armed Bandit Approach
Bibliographic record
Abstract
Efficient routing protocols are required in opportunistic networks due to the inherent challenges of sporadic connectivity, transmission delays, and dynamic network topologies. In these networks, information is forwarded and disseminated among smart devices based on opportunistic contacts driven primarily by network dynamics and user mobility. This paper introducesLearnRouter, a dynamic reinforcement learning-based routing algorithm designed to improve the message delivery ratio while minimizing end-to-end delay in opportunistic networks. Existing routing protocols use static strategies or fixed replication schemes, leading to resource utilization and high message drop rates. LearnRouter addresses these challenges by incorporating the multi-armed bandit framework and utilizing the Upper Confidence Bound algorithm to make more intelligent and data-driven forwarding decisions. The proposed approach enables the protocol to adapt continuously to network changes while balancing the trade-off between exploration and exploitation. The simulation results show that LearnRouter surpasses end-to-end delay and message delivery ratio in comparison with Direct Delivery, Spray and Wait, CBR, SimRouter, RL-Prophet, and K-DQLR in opportunistic and dynamic environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".