Distributed Caching Enabled Peak Traffic Reduction in Ultra-Dense IoT Networks
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Bibliographic record
Abstract
The proliferation of massive machine-type communications devices and their random and intermittent transmissions have brought the new challenge of sporadic access-network congestion in ultra-dense Internet of Things (IoT) networks. To address this issue, we propose an innovative approach of peak traffic reduction within the access network by utilizing distributed cache of IoT devices to coordinate their sporadic transmissions. The proposed technique is realized by employing a novel uplink transmission scheduling based on delay adaptation, in which distributed IoT devices adjust their transmission timings by utilizing embedded caching. An optimization problem is formulated for the minimization of peak data rate demand subject to delay tolerance levels, and is solved for the 3GPP-based traffic models by employing a gradient descent-based algorithm. Our results show that the proposed scheme can significantly reduce the peak data traffic in ultra-dense IoT networks.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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