UAV-Assisted Data Transmission in Blockchain-Enabled M2M Communications with Mobile Edge Computing
Bibliographic record
Abstract
Recently, the development of the internet of Things (ioT) provides plenty of opportunities and challenges in various fields. As an essential part of ioT, machine-to-machine (M2M) communications open a novel way that machine-type communication devices (MTCDs) are connected and communicated without any human intervention. However, when ioT infrastructures are destroyed, network services will be disrupted. Then it is difficult for the MTCDs located in remote areas to restore communication by themselves immediately. To cope with these problems, in this article, we introduce some promising technologies such as unmanned aerial vehicles (UAV), blockchain and mobile edge computing (MEC) to ensure data transmission, security and reliability in damaged M2M communications networks. Meanwhile, we propose a joint optimization framework to maximize both data computation capacity and throughput of blockchain systems, and formulate it as a Markov decision process (MDP). in order to solve the dynamic and complicated optimization problem, dueling deep Q-network (DQN) is adopted, so that the optimal selection and decision can be made to achieve maximum system rewards. Simulation results with different system parameters show that our proposed framework can improve the system performance effectively compared to the existing schemes. Finally, open research issues and challenges are discussed for UAV-assisted M2M communications.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".