Experimental QoS Optimization for Haptic Communication Over Tactile Internet
Bibliographic record
Abstract
So far, most haptic applications are standalone systems or endeavors to provide collaborated haptic virtual environments. With the emergence of the Tactile Internet (TI), ultra-low-delay and ultra-high-reliable communications will enable a paradigm shift from traditional content-oriented communication to control-oriented communication. Specifically, The human-in-the-loop Tactile Internet enables the vision of delivering human skills e.g, feeling and manipulating, in addition to the human knowledge e.g., seeing and hearing, remotely, adding more life to the Internet of skills. Within this paradigm, human multisensory information for interaction and communication with the remote environment needs to be exchanged. In this paper, we present an experimental study to optimize objective quality evaluation for multimodal communication especially haptic, over the Internet. For that purpose, a simulated haptic model based on the ALPHAN protocol was implemented on Riverbed modular to generate real haptic traffics over an infrastructure that mimics the TI, the model was used to select appropriate Diffserv QoS solutions in a large-scale collaborated haptic environment. The outcome of the study found that deploying custom queuing with low latency queue (LLQ) or Priority Queuing (PQ) in conjunction with ALPHAN protocol can be used to dramatically enhance the network performance of haptic communication.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".