Computation Offloading and Content Caching in Wireless Blockchain Networks With Mobile Edge Computing
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
Blockchain technology has been applied in a variety of fields due to its capability of establishing trust in a decentralized fashion. However, the application of blockchain in wireless mobile networks is hindered by a major challenge brought by the proof-of-work puzzle during the mining process, which sets a high demand for the computational capability and storage availability in mobile devices. To address this problem, we propose a novel mobile edge computing (MEC) enabled wireless blockchain framework where the computation-intensive mining tasks can be offloaded to nearby edge computing nodes and the cryptographic hashes of blocks can be cached in the MEC server. Particularly, two offloading modes are considered, i.e., offloaded to the nearby access point or a group of nearby users. First, we conduct the performance analysis of each mode with stochastic geometry methods. Then, the joint offloading decision and caching strategy is formulated as an optimization problem. Furthermore, an alternating direction method of multipliers based algorithm is utilized to solve the problem in a distributed manner. Finally, simulation results demonstrate the effectiveness of our proposed scheme.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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