Deep Q-Network Dueling-Based Opportunistic Data Transmission in Blockchain-Enabled M2M Communication
Bibliographic record
Abstract
The growth of the opportunistic network (OppNet) in recent years has created a variety of opportunities and concerns. Machine-to-machine (M2M) communications, which are a critical component of OppNet, provide a novel means for connecting and communicating among machine-type communication devices (MTCDs) without the need for human interaction. OppNet data play a significant role in M2M communications and it emphasizes more powerful data storage, computation, processing, as well as the security and stability of data transfer. This paper proposes a joint optimization framework for dueling Deep Q-network (DQN)-based opportunistic data transmission in M2M communication using blockchain technology. The best choice and decision of caching servers, blockchain systems, and computing nodes, can be made in accordance with the dynamic decision-making process by DQN, a decision to increase the system incentives, including high data processing efficiency, lower cost, and improved data interaction security. Simulation results using various parameters demonstrate that the proposed model has more benefits and is more efficient than the existing random, greedy, and Conventional DQN benchmark schemes.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".