Trust verification in information fusion-based autonomous monitoring systems using 9-valued logic

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.