The paper proposes an agent-based model for the early detection of trust anomalies in Industrial Internet of Things networks operating under dynamic conditions. The relevance of the study is determined by the need for timely identification of abnormal node behavior that may indicate communication instability, compromised operation, false data injection, or data inconsistency before explicit failures occur. The proposed model is based on the interaction of local monitoring agents, trust evaluation agents, and a coordination agent, which together provide distributed observation of node behavior and cooperative interpretation of suspicious deviations. A compact set of trust-related indicators is used, including data consistency, communication stability, packet delivery regularity, response delay, and interaction reliability. To formalize anomaly detection, an integrated trust score and a threshold-based decision rule are introduced. An illustrative synthetic scenario is presented to demonstrate the operation of the proposed model under normal, degraded, and anomalous node states. The results show that gradual degradation of trust-related indicators leads to a decrease in the trust score and enables early anomaly signaling before explicit system-level failure becomes evident. The proposed approach can be used as a conceptual basis for distributed trust monitoring in dynamic Industrial IoT environments.
Алькема et al. (Tue,) studied this question.