5G-TSN/DetNet Integrated Networks for Industrial IoT: A Machine Learning Framework to Balance Reliability and Bandwidth Utilization
Bibliographic record
Abstract
In the growing field of the Industrial Internet of Things (IIoT), the integration of 5 G networks with TimeSensitive Networking (TSN) and Deterministic Networking (DetNet) promises unprecedented reliability in data transmission. This paper introduces a Machine Learning (ML)-based methodological framework to optimize the balance between network reliability and bandwidth utilization in 5G-TSN/DetNet integrated networks. Our framework enhances network performance by dynamically de/activating the TSN/DetNet reliability functions based on real-time analysis of 5 G radio conditions. We employ a clustering algorithm to categorize radio measurements into Green, Orange, and Red conditions, with a hysteresis logic to ensure stable state transitions and avoid premature or unnecessary TSN/DetNet reliability toggling. Proof-of-concept experimental results obtained from a 5 G testbed emulating an IIoT device with dual 5G User Equipment demonstrate the benefits of our approach. Specifically, we show that the framework can maintain high data transmission reliability with zero packet loss, even under fluctuating network conditions, while efficiently utilizing available bandwidth. The findings underscore the potential of ML in automating and enhancing decision-making processes in next-generation industrial networks, paving the way for more resilient and adaptive communication for IIOT communications. The integration of such frameworks into actual 3GPP systems is suggested as a future direction for real-world application.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".