A Novel Fog Computing Enabled Temporal Data Reduction Scheme in IoT Systems
Bibliographic record
Abstract
The recent advancement of Internet of Things (IoT) technologies has enabled many emerging applications, including smart building and connected vehicles. These advanced applications generate massive amount of data at the edge of IoT networks, which usually need to be relayed to a remote data center for further real-time processing. However, uploading all these IoT data to the cloud platform imposes a heavy burden on the underlying network. The unavoidable long delay from data exchange and processing significantly reduces the time-responsiveness of real-time IoT applications. Recently, fog computing has been introduced to IoT applications as an intermediate between end devices and cloud for primary IoT data processing. In this paper, a temporal IoT data reduction scheme through fog computing is proposed to reduce the total amount of IoT data uploaded to the cloud. More specifically, IoT data are first modeled as multivariate normal distribution by the cloud. Dual Kalman filters (KF) with identical parameters are then deployed at both the cloud and fog platforms. The same predictions are simultaneously triggered by the dual KFs at both platforms. Only the measured IoT data out of predicted range are further uploaded from fog to cloud. Otherwise, predicted values at both platforms are used instead of measurements. A simple prototype IoT system is developed for performance evaluation. Experimental results indicate that the proposed scheme significantly reduces the number of packets uploaded to the cloud platform with high data accuracy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".