Intelligent Content Caching and User Association in Mobile Edge Computing Networks for Smart Cities
Why this work is in the frame
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Bibliographic record
Abstract
To support rapidly increasing multimedia services of smart cities, mobile edge computing (MEC) networks can significantly reduce content acquisition latency. However, due to user mobility and the possibility of re-association, it is challenging to obtain a proper content caching and user association policy. In this article, we investigate the issue of joint content caching and user association with high user mobility in MEC networks by minimizing content acquisition latency and handover latency. To address the problem, we optimize the original mixed time-scale problem in two stages: long-time scale content caching and short-time scale user association. we propose an intelligent content caching framework based on a weighted latent factor model to determine content caching policy at each time frame. Then we design a matching theory-based lazy re-association strategy at each time slot. Simulation results on real-world MEC networks demonstrate the effectiveness of the proposed framework.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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