The rapid development of decentralized IoT systems has made the device admission process increasingly difficult, especially in environments where there is no central authority that can prevent malicious devices from influencing distributed decisions in IoT networks. Currently, most blockchain-based solutions and trust mechanisms focus separately on either reputation evaluation or access control, without clearly defining how distributed entities can jointly evaluate, validate, and enforce admission decisions in dynamic IoT environments. Many admission processes rely on fixed thresholds for trust evaluation or implicit assumptions about device behavior, which can affect the scalability and consistency of decisions. In this paper, a decentralized admission mechanism for IoT devices based on blockchain is proposed, which combines multi-attribute evaluation, trust-weighted distributed voting, conditional event-aware verification, and efficient event validation in a single framework. Candidate IoT devices are evaluated using both reliability and performance information from trusted vendors and data obtained from their behavior during network interaction functions. Admission decisions are made collaboratively, through a voting mechanism in which the influence of each IoT device depends on its trust score. When suspicious behavior is detected, the conditional event-aware mechanism uses independently collected evidence as the reference for subsequent trust and weight updates, limiting the reinforcement of malicious evaluator influence. For efficient recording and validation of admission events, a Hierarchical Multi-level Merkle Reduction (HMMR) structure is introduced, providing lower memory usage and shorter construction time compared with the evaluated Merkle Tree and Merkle Mountain Range implementations, while exhibiting a trade-off in proof-generation time and proof size. The obtained results show that admission processing time ranges from 7.58 ms for 50 devices to 6.56 s for 20,000 devices. The proposed mechanism can efficiently combine IoT device evaluation, distributed decision-making, adaptive trust management, and validation into a single solution for managing device admission in large-scale decentralized IoT architectures. The evaluation was conducted in a controlled cloud-based emulation environment; therefore, hardware-specific resource consumption and heterogeneous real-world network conditions remain to be validated in future work.
