CrossTrust-IoT: An Adaptive Reinforcement Learning Framework for Cross-Chain Trust in Resource-Constrained IoT Networks

Decentralized trust systems harbor a paradoxical vulnerability: the emergent rise of a ‘Trust Aristocracy’ that erodes their foundational purpose. While existing models can detect tactical threats, they lack mechanisms to manage this emergent, strategic vulnerability. This paper introduces CrossTrust-IoT, an adaptive framework that presents a paradigm shift from passive observation to active control of decentralization. Our core contribution is the design of the first trust management system to operationalize the Gini coefficient as an evolutionary control variable, thereby transforming a descriptive socio-economic metric into an active defense against influence concentration. We propose a novel dual-loop architecture: a tactical Reinforcement Learning TrustRank (RLTR) for node-level threat detection and a strategic Self-Evolving Trust Architecture (SETA) that monitors the global trust distribution. The SETA’s evolutionary algorithm uses the Gini coefficient directly in its fitness function to penalize influence concentration, actively steering the system’s parameters towards a state of balanced security and decentralization. This mechanism is complemented by privacy-preserving federated learning to train the RLTR model. We validate our framework on the CICIoT2023 dataset and in dynamic agent-based simulations, demonstrating that CrossTrust-IoT achieves a high F1-score of 0.91 for threat detection while preventing trust aristocracy, a failure mode to which even our framework succumbs when the Gini penalty is disabled. This work pioneers a new approach to engineering resilient trust ecosystems by treating decentralization not as a static property, but as a dynamic objective to be actively managed.