Enhanced efficiency and security in cross-chain transmission of blockchain internet of ports through multi-feature-based joint learning

The rapid development of Blockchain Internet of Things (IoT) has intensified the need for efficient and secure cross-chain transmission across heterogeneous systems. However, traditional cross-chain methods, such as hash time-locked contracts and relay chains, focus primarily on security and correctness while neglecting performance optimization. This limitation is particularly pronounced in high-dynamic environments like port areas, where network congestion, high latency, and uneven resource utilization are prevalent challenges. To address these gaps, this study proposes a novel load-adaptive cross-chain control method tailored for Blockchain IoT systems in port areas. The proposed method integrates multi-feature joint learning with adaptive multi-channel joint bus control, enabling dynamic resource allocation and interference suppression for enhanced transmission efficiency. Furthermore, a distributed intelligent scheduling mechanism is introduced to improve scalability and stability under high-concurrency conditions by decentralizing task coordination across blockchain nodes. Additionally, federated learning is employed to optimize cross-chain communication while preserving data privacy, ensuring secure and collaborative optimization in multi-party environments. Extensive simulations validate the effectiveness of the proposed approach, demonstrating significant improvements in throughput, latency, and packet loss rate compared to traditional centralized methods. The results highlight the method's ability to balance dynamic network loads, minimize interference, and adapt to real-time conditions. This work bridges the gap between performance-oriented optimizations and privacy-preserving mechanisms, offering a scalable and secure solution for Blockchain IoT systems in complex and dynamic environments.