Digi-Infrastructure: Digital Twin-Enabled Traffic Shaping with Low-Latency for 6G Smart Cities
Bibliographic record
Abstract
Digital twin (DT)-based smart cities are anticipated to achieve seamless integration between physical and digital objects to satisfy an enormous number of users across all domains. Therefore, the infrastructure of 6G smart cities has become an important topic. Many types and data priorities exist in 6G smart cities; therefore, data traffic management is challenging. Current solutions may face challenges adjusting to swiftly evolving network circumstances and the unexpected rise of time-sensitive data. They require flexibility to handle non-periodic, unforeseen, and time-sensitive traffic, such as mission-critical applications. While current research explores the combination of Time-Sensitive Networking (TSN) and 5G, the evolution to 6G also necessitates the integration of TSN and DT technology to achieve deterministic networking. Therefore, taking advantage of DT in data traffic management, we propose a DT-enabled traffic shaping architecture called Digi-infrastructure, consisting of an intelligent traffic shaper inspired by TSN. Our proposed shaper comprises two components: the first component is a frame classification method established on Deep Reinforcement Learning (DRL) to address the dynamic scheduling problem by minimising the end-to-end delay. The second component is an intelligent gate control mechanism that considers the time, queue status and specified transmission time of traffic classes according to priority based on latency requirements without using a gate control list or timing data gate control. Finally, our solution improves infrastructure connectivity, efficiency, and latency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".