Selection Algorithm for Reducing IoT Service Delay in the Smart Factory
Bibliographic record
Abstract
The smart factory is a concept to express the ultimate goal of digitization in manufacturing. The most common definition of a smart factory is a highly digital store floor that collects and shares data on a continuous basis through connected devices and production systems. the proposed system focuses on reducing the delay by using hybrid computing, cloud, and edge servers. The purpose of this study is to investigate Real-time requirements using the Selection Algorithm. The factory is represented by a group of sensors that send data to three servers. (Two are edge servers with the same copies of data and rules, and the third is the cloud). When the sensor's reading reaches the edge servers, scheduling time an selection algorithm is implemented through which a single edge server receives data selecting the least delay the highest priority. Alternatively, if there is any problem or malfunction affects one of the edge servers, the second can complete its work without the need to stop the factory. In turn, the delay is reduced, and the factory performance is improved. When the delay time is reduced, the response time is improved and the service quality will be enhanced.
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How this classification was reachedexpand
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 | 0.006 | 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 | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".