A Fuzzy-based System for assessment of relational trust in IoT and social networks

Abstract

In modern digital ecosystems, trust plays a critical role in ensuring secure and reliable interactions across various entities, including humans and machines. As technologies such as 5G wireless networks and the Internet of Things (IoT) drive unprecedented complexity in these environments, the demand for flexible and effective trust evaluation systems has grown exponentially. This paper presents a Fuzzy-based System for Assessment of Relational Trust (FSART), which utilizes fuzzy logic to evaluate trust levels between entities. We implemented two models (FSARTM1 and FSARTM2) considering three parameters: Influence (If), Importance (Ip), and Similarity (Sm) for FSARTM1, while for FSARTM2 we considered Reputation (Rp) as an additional parameter. The simulation results indicate that increasing the values of these parameters leads to a corresponding increase in Relational Trust (RT). For FSARTM1, when Ip is 0.9, all RT values exceed 0.5, while in FSARTM2 when If is 0.9 and moderate values of other parameters, RT consistently remains above 0.5. These results suggest that FSARTM2 provides a more accurate assessment by incorporating Rp, making it a more robust tool for trust evaluation in complex digital ecosystems, including social media, collaborative environments, and IoT systems.