A Machine Learning Framework for Handling Delayed/Lost Packets in Tactile Internet Remote Robotic Surgery
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
Remote robotic surgery, one of the most interesting 5G-enabled Tactile Internet applications, requires an ultra-low latency of 1 ms and high reliability of 99.999%. Communication disruptions such as packet loss and delay in remote robotic surgery can prevent messages between the surgeon and patient from arriving within the required deadline. In this paper, we advocate for scalable Gaussian process regression (GPR) to predict the contents of delayed and/or lost messages. Specifically, two kernel versions of the sequential randomized low-rank and sparse matrix factorization method ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -SRLSMF and SRLSMF) are proposed to scale GPR and address the issue of delayed and/or lost data in the training dataset. Given that the standard eigen decomposition for online GPR covariance update is cost-prohibitive, we employ incremental eigen decomposition in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -SRLSMF and SRLSMF GPR methods. Simulations were conducted to evaluate the performance of our proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell _{1}$ </tex-math></inline-formula> -SRLSMF and SRLSMF GPR methods to compensate for the detrimental impacts of excessive delay and packet loss associated with 5G-enabled Tactile Internet remote robotic surgery. The results demonstrate that our proposed framework can outperform state-of-the-art approaches in terms of haptic data generalization performance. Finally, we assess the proposed framework’s ability to meet the Tactile Internet requirement for remote robotic surgery and discuss future research directions.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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