Blockchain‐Enabled Task Offloading and Resource Allocation in Fog Computing Networks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To address the data security and user privacy issues in the task offloading process and resource allocation of the fog computing network, a blockchain‐enabled fog computing network task offloading model is proposed in this paper. Furthermore, to reduce the network utility which is defined as the total energy consumption of the fog computing network and the total delay of the blockchain network, a blockchain‐enabled fog computing network task offloading and resource allocation algorithm (TR‐BFCN) is proposed to jointly optimize the task offloading decision and resource allocation. Finally, the original nonconvex optimization problem is converted into two suboptimization problems, namely, task offloading decisions and computational resource allocations. Moreover, a two‐stage Stackelberg game model is designed to obtain the optimal amount of purchased resource and the optimal resource pricing. Simulation results show that the proposed TR‐BFCN algorithm can effectively reduce the network utility compared with other algorithms.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it