A Deep Reinforcement Learning-Based Caching Strategy for Internet of Things
Why this work is in the frame
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Bibliographic record
Abstract
With the continuous growth of the Internet of Things (IoT), the specific needs of these networks are becoming more evident. Transient data generated and limited energy resources are two of the characteristics of IoT networks that impose some limitations. Moreover, the conventional quality of service requirements, such as minimum delay, are still needed in these networks. By implementing an effective caching policy, it is possible to meet the current demands while easing the specific limitations of IoT networks. By leveraging deep reinforcement learning technique, without the need of prior knowledge of the contents' popularity, contents lifetimes or any other type of contextual information, we have managed to develop a caching policy which increases the cache hit rate and decreases the energy consumption of IoT devices while simultaneously considering the limited lifetime of the data contents. The simulation results show that our proposed method outperforms the conventional Least Recently Used (LRU) method by considerable margins in all aspects.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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