ART: Active recognition trust mechanism for Augmented Intelligence of Things (AIoT) in smart enterprise systems
Bibliographic record
Abstract
In smart enterprise systems, augmented IoT can efficiently improve the decision-making, handling, and generation of a huge amount of information during communication. However, Augmented Internet-of-Things (AIoT) leads to various security and trust issues when transmitting information through intermediate devices. In a case where malicious devices can easily integrate with legitimate devices, it can further affect and interfere with the overall performance of the network system. Though various security surveys have been illustrated and schemes have been proposed by scientists, however, all of them are in their early stages. This paper proposes a trusted decision-making mechanism called Active Recognition Trust (ART), using AIoT for handling smart enterprise systems. The proposed mechanism integrates active recognition and associated reference mechanisms to improve the efficiency and effectiveness of the secure transmission process by computing a trust value for each device using impact factors of function fusion systems before information exchanges. Simulation results show that the proposed mechanism can efficiently enhance performance while improving the accuracy of recognizing legitimate devices by reducing or eliminating interference from malicious devices. The proposed mechanism is evaluated using the transmission ratio, identification accuracy, average trust, and run cycle compared to the existing mechanisms. Further, the proposed mechanism achieves approximately 89% better improvement than the baseline approach.
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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".