Security Management of Horizontal IoT Platforms: A Survey and Comparison
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
With the rise of Industry 4.0, horizontal Internet of Things (IoT) platforms are becoming a standardized approach for managing interoperability within complex and heterogeneous IoT systems. Horizontal IoT platforms are software solutions that provide overall IoT system orchestration and management. They work to facilitate IoT services and resources, where security management remains one of the main challenges. This article provides a survey and comparison of security management in IoT systems using horizontal IoT platforms. For this purpose, we first define and compare vertical and horizontal IoT platforms. Although vertical IoT platforms provide solutions to many industries, horizontal IoT platforms improve system connectivity by interconnecting multiple vertical domains. We then describe the security management functionalities of horizontal IoT platforms. With these in mind, we perform a comparative study on the current state of security management approaches of existing horizontal IoT platforms. Particularly, we survey and compare the security management features of the selected standard-based reference implementations. Through discussions, we cover concerns that researchers and developers should be aware of when selecting specific reference implementations for their works. Finally, we identify open issues in the existing security management principles of these reference implementations to be addressed in future studies and practical implementations.
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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 | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.000 | 0.001 |
| 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