Risk-Calibrated Trust Modeling for Secure Distributed Intelligence across Wireless, AI, and Blockchain Ecosystems
DOI:
https://doi.org/10.66238/ijrss.v6i2.119Keywords:
Distributed Intelligence, Trust Modeling, Blockchain Security, Federated Learning, Wireless NetworksAbstract
The rapid convergence of wireless communication networks, artificial intelligence, and blockchain technologies has facilitated the emergence of highly distributed intelligence ecosystems. While these integrated ecosystems offer unprecedented capabilities in data processing, automated decision-making, and decentralized consensus, they simultaneously introduce complex security vulnerabilities. Traditional trust models, which typically rely on static credentialing or deterministic behavioral scoring, are insufficient to address the dynamic, multidimensional risk landscapes inherent in such heterogeneous environments. This paper proposes a comprehensive framework for Risk-Calibrated Trust Modeling designed to secure distributed intelligence operations. By continuously evaluating contextual risk factors across the physical wireless layer, the algorithmic artificial intelligence layer, and the decentralized blockchain ledger layer, the proposed model dynamically modulates trust scores to preemptively mitigate threats such as data poisoning, Sybil attacks, and consensus manipulation. Through rigorous mathematical formulation and extensive simulation analysis, we demonstrate that incorporating risk calibration into trust decay and propagation algorithms significantly enhances network resilience. The methodology integrates real-time channel state information from wireless nodes, localized model updates from federated learning agents, and smart contract execution metrics to establish a holistic, probabilistic trust metric. Results indicate that the risk-calibrated approach reduces the impact of malicious actors on systemic integrity while maintaining optimal throughput and consensus efficiency, providing a robust foundation for future decentralized autonomous networks
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