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 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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".