Internet of Robotic Things Evolution, Standards and Data Interoperability Best Practices for the Next Generation of Artificial Intelligence‐Powered Systems
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
ABSTRACT The Internet of Robotic Things (IoRT) represents the rise of a new paradigm enabling robots to serve not only as autonomous units but also as intelligent interconnected entities that can interact, collaborate, and share information through the edge, cloud and other data networks. IoRT is a technological progress and the fusion of Robotics with the Internet of Things (IoT), artificial intelligence (AI), and edge‐Computing, IoRT can benefit from the next‐generation spatial web, Web 4.0 (the intelligent immersive knowledge Web), by enhancing data processing, situational awareness, and integration with immersive technologies, software‐defined automation (SDA), and spatial computing technologies. Semantic Web and Web 4.0 technologies are becoming common in robotics projects for exchanging data and enabling data set interoperability. The main challenge is to upgrade how robotic things interact with each other and their environment in a more situation‐aware fashion, enabling IoRT situation‐aware capabilities. This paper reviews the definition of IoRT considering the latest developments in sensor technology and data management systems and uses a novel survey methodology to find, classify, and reuse robotic expertise and present it to the community and engineering experts. The survey is shared through the LOV4IoT‐Robotics ontology catalog, which is available online. This catalog demonstrates how best practices for data sharing and data set interoperability are also used to extract robotic knowledge semi‐automatically. A set of relevant semantic‐enabled projects designed by domain experts that focused on extracting robotic knowledge was included.
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 | 0.001 |
| 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