Enhanced Efficiency and Security in Cross-Chain Transmission of Blockchain Internet of Ports through Multi-Feature-Based Joint Learning

Abstract

To enhance the timeliness of cross-chain transmission (CCT) in port blockchain Internet of Things (IoT), this study proposes a load adaptive control method tailored for CCT within port blockchain IoT networks. It establishes a balanced configuration model for CCT links within the blockchain IoT framework in port areas. The method facilitates compensation and adjustment of joint autocorrelation dynamic parameters pertinent to blockchain IoT devices within port environments through load-balancing scheduling techniques. Furthermore, it examines the correlation constraint parameter characteristics governing CCT control of blockchain IoT within port zones. Leveraging parameter estimation and priority scheduling methodologies for blockchain IoT CCT links, the study employs multi-feature joint learning (MFJL) to construct a load estimation model specific to CCT within port blockchain IoT settings. Moreover, the interference suppression of blockchain IoT CCT in port areas is addressed via the MFJL algorithm, which also facilitates the analysis of distinct characteristic components of cross-chain switching channels. The study implements adaptive multi-channel joint bus control and link balance designs for blockchain IoT CCT within port locales through grid scheduling and cross-chain parameter fusion methods. These endeavours aim to enhance the balance and real-time performance of blockchain IoT CCT within port zones. Simulation outcomes demonstrate notable efficacy, with the average input value of cross-chain energy efficiency for blockchain IoT in port areas reaching 5Mbps, the highest output value peaking at 9Mbps, and the average CCT queue length hovering around 1dB. Additionally, the energy consumption of blockchain IoT devices in port areas is observed to decrease by 1KJ by adopting this methodology.