The Tactile Internet over 5G FiWi Architectures
Bibliographic record
Abstract
The Tactile Internet (TI) holds great promise to have a profound socio-economic impact on a broad array of applications in our everyday life, ranging from industry automation and transport systems to healthcare, telesurgery, and education. This chapter focuses on the proposed fiber-wireless (FiWi) enhanced LTE-Advanced heterogeneous networks, on which emerging 5G systems are envisioned to rely. It presents in-depth technical insights into realizing human-in-the-loop centric teleoperation TI over FiWi enhanced networks, including trace-based haptic traffic modeling, perceptual deadband coding, haptic sample forecasting, and trace-driven simulations. Collaborative computing based human-to-robot communications in advanced FiWi based TI infrastructures may offer significant benefits in terms of improved task execution time, cost reduction, and scalability. Collaboration and communication among humans-are-better-at/machines-are-better-at members is important to cope with dynamic changes in the task environment. TI traffic is expected to require the underlying communication networks to undergo profound modifications, both from architectural and medium access control viewpoints.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".