Smart Controller Integrated with MQTT Broker Based on Machina Learning Techniques
Bibliographic record
Abstract
Massive amounts of heterogeneous data are produced by Internet of Things (IoT) devices utilized in daily life and numerous fields, and these data streams need to be stored, processed, analyzed, and transmitted to the cloud. It usually suffers from missing values and anomalies; system services also suffer from congestion due to slow processors, resulting in low throughput, a high response time, slow decision-making, and data loss, resulting in low quality of service and the deterioration of the system's performance. In this study, propose to integrate the smart controller (SC) with the Message Queuing Telemetry Transport (MQTT) broker and services in the fog node to make decisions automatically to prevent congestion in the system's services and speed up the processing. The IoT stream is inspected in the services for anomalies using one-class support vector machines (OCSVM). Then, using the integrating technique of principal component analysis (PCA) and the k-nearest neighbors (KNN) algorithm in the SC, obtain the best prediction of the efficient number of services that must be deployed in the system. The operating model proposed showed significantly stable system performance in terms of throughput, latency, response time, the amount of data loss, and preventing congestion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".