Distributed Slot Allocation in Capillary Gateways for Internet of Things Networks
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Bibliographic record
Abstract
The applications and usage of the internet is expanding on a daily basis and the Internet of Things (IoT) is fast becoming the new approach for incorporating the internet into our personal, professional and social lives. IoT enables a wide variety of devices to inter-operate through the existing internet infrastructure. Capillary networks are proposed as a fundamental part of loT development, and will enable local sensor and devices to connect efficiently with other ubiquitous communication networks such as cellular systems. In this paper, we apply the Q-learning algorithm for the scheduling of capillary gateways for (M2M) communication in IoT networks. Q-learning algorithm is used to select conflict-free slot assignment for these gateways in a self-organizing manner. We analyze the performance of the proposed algorithm with respect to learning rates and rewards.
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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