Passive Network Synchronization Based on Concurrent Observations in Industrial IoT Systems
Why this work is in the frame
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Bibliographic record
Abstract
Accurate network synchronization is crucial to orchestrate distributed infrastructures in Industrial Internet of Things (IIoT) systems for accomplishing network-wide tight temporal collaboration. Traditional clock synchronization can be achieved with extensive exchanges of explicit timestamps for estimating clock offsets, which becomes impractical due to high overhead with the expansion of the network scale. The performance of conventional synchronization will also be dramatically deteriorated due to various uncertainties of IIoT networks. In this article, we propose a passive network synchronization scheme based on concurrent passive observations to calibrate the distributed clocks in IIoT systems while significantly reducing the explicit interactions and network resource consumption during synchronization. By processing the physical phenomena observed concurrently by a group of selected IIoT devices, the local clock offsets of the passive observing devices can be efficiently estimated according to the common time reference linked to the event observed. Multiple relay nodes are further coordinated by the cloud center to disseminate the reference time information throughout the IIoT system. Simulation results demonstrate that by utilizing a series of concurrent observations with efficient coordination, the proposed scheme can achieve accurate and reliable network synchronization for large-scale IIoT systems with significantly reduced network overhead.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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