Supporting consumer services in a deterministic industrial internet core network
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A convergence is occurring in the networking world. Industrial networks currently provide deterministic services in robotic factories and aircraft, while the best effort Internet of Things provides best effort services for consumers. We argue that a convergence should occur, and that a future converged Industrial Internet of Things (IIoT) should support both best effort and deterministic services, with very low latency and jitter. This article presents the design of a deterministic IIoT core network consisting of many simple deterministic packet switches configured by an SDN control plane. The use of deterministic communications can reduce router buffer sizes by a factor of ≥ 1000, and can reduce end-to-end latencies to the speed of light in fiber. A speed-of-light deterministic core network can have a profound impact on virtually all consumer services such as multimedia distribution, e-Commerce, and cloud computing or gaming systems. Highly aggregated video streams can be delivered over a deterministic virtual network with very high link utilization (≤ 100 percent), very low packet jitter (≤ 10 μs), and zero congestion. In addition to improving consumer services, a converged deterministic IIoT core network can save billions of dollars per year as a result of significantly improved network utilization and energy efficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 it