Verifying Timed Commitment Specifications for IoT-Cloud Systems with Uncertainty
Bibliographic record
Abstract
Cloud Computing plays an essential role in meeting the increasing demand for large data storage and infrastructures in IoT applications. The applications of IoT-Cloud are in an exponential rise in the number of interacting components with different interaction protocols within open and uncertain environments. The main challenge that faces these applications is ensuring their reliability and efficiency. This paper proposes a scalable verification approach for IoT-Cloud applications in uncertainty-characterised settings with timed commitments using three-valued model checking. Timed commitments are powerful artifacts that capture flexible and rich interaction protocols. We use a new logic for reasoning about uncertainty in commitment protocols, model a smart contract-based IoT mortgage system with commitments under uncertain settings, introduce a set of specifications, and implement a verification framework of our model against its specifications using a transformation algorithm and the ${MCMAS}_{+}$ model checker. Finally, we report and discuss our experimental results.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".