Trust verification in information fusion-based autonomous monitoring systems using 9-valued logic
Bibliographic record
Abstract
Ensuring the correctness and reliability of communication in multi-source data autonomous monitoring systems is a critical challenge, particularly in wireless and IoT-enabled environments, where trust and decision-making play a crucial role. This paper introduces a novel nine-valued verification framework designed to verify trust-based protocols in autonomous systems under multi-source data settings. The proposed framework, 9V-TCTL, provides a formal modeling approach that captures complex trust dynamics and fusion-driven decision-making processes, particularly in multi-UAV fire detection and IoT-based monitoring applications. To support the verification of 9V-TCTL models, we developed 9V-VISPL, an extended version of the ISPL language, which converts these models into a format compatible with the MCMAS-T verifier. Additionally, we propose a reduction algorithm that preserves the syntax and semantics of 9V-TCTL while transforming it into two-valued logic, enabling the use of existing efficient model verifiers. Our implementation, 9V-Checker, is provided as an open-source tool with extensive documentation and experimental validation. We compare our approach with state-of-the-art verification tools, demonstrating its advantages in scalability, transformation efficiency, and trust expressiveness. Through scalability and reliability testing in multi-source data autonomous monitoring systems, we demonstrate the effectiveness of our approach in enhancing trust verification and fusion-based decision-making in multi-sensor environments.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| 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".