Topology Design for Robust IoT Data Gathering via Bayesian Networks
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Bibliographic record
Abstract
Internet of Things (IoT) systems have become the critical platform to enable a wide variety of smart applications. During IoT data gathering over wireless network, data may be missing due to the constraints of sensors as well as the reliability of communications. From a graph signal processing perspective, recovery of missing data may be strongly affected by the IoT system topology, which can be characterized by a directed adjacency matrix. To guarantee a robust data gathering, we propose a novel method in this paper to design the optimal topology for IoT networks via Bayesian networks, where the designed directed adjacency matrix is with orthogonal graph frequency components. Moreover, the gathering of IoT data becomes sparser in the graph frequency domain using the designed adjacency matrix and may hence improve the recovery performance of missing data. Experimental results show that our proposed methods outperform several existing algorithms.
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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 | 0.001 |
| 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