Leveraging Tactile Internet Cognizance and Operation via IoT and Edge Technologies
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
The Tactile Internet (TI) is building on the premise of remote operation in perceived real-time, and enables a plethora of applications that involve immersive interactions. As we build a future for globalizing skills, delivering haptic feedback across continents, and immersing users in remote environments, we are faced with significant challenges in understanding the context of Tactile Internet interactions, which we refer to as tactile cognizance. The challenge of understanding a remote terminals' context impacts not only the quality and depth of haptic feedback, but our ability to deliver perceived real-time operation. That is, as we develop AI techniques to compensate for the inevitable delay in remote operation, we need more information about a terminal's context and interactions to improve our prediction of movement and feedback. The Internet of Things (IoT) is promising to interconnect billions of sensors, and augment multiple tiers of cognition to expedite and fine-tune sensory acquisition from heterogeneous contexts. In this paper, we will survey recent developments in the IoT, and novel techniques for cloudlet-based cyber foraging (i.e., edge computing) to project how Tactile Internet interactions could benefit from IoT contextualization. We present a taxonomy of edge IoT systems designed for rapid data acquisition, with an emphasis on systems that prioritize stringent reliability and latency mandates. This paper builds on edge computing techniques to propose a framework for multi-tiered cognition in the Tactile Internet to feed its signaling systems, and how future TI codecs could embed contextual information in haptic feedback.
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 | 0.000 |
| 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