Low-Overhead Data Synchronization Enabled by Prescheduled Task Period in Time-Sensitive IoT Systems
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Time-sensitive applications in Internet of Things (IoT) systems rely heavily on the temporal coherence among its distributed constituents during data fusion and analysis. The inconsistent clock output inherent to the unstable and heterogeneous clock oscillator embedded at each IoT device will inevitably lead to inaccurate data processing and deteriorated overall performance. In this paper, a low-overhead data synchronization scheme is proposed to achieve accurate temporal consistency prior to fusing the massive data collected from the distributed IoT devices. More specifically, a task period is scheduled for each sensor device to deliver the sampled data to SN. By comparing the difference between the predefined period and the real observed one, the clock parameters can be estimated accurately so that the misalignment of the data can be compensated accordingly. Simulation results show that the proposed scheme can enhance the data fusion accuracy with significantly reduced network overhead.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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