Deep Learning Based Autonomous Transport System for Secure Vehicle and Cargo Matching

【Author】 Shanthi, T.; Ramprasath, M.; Kavitha, A.; Muruganantham, T.

【Source】INTELLIGENT AUTOMATION AND SOFT COMPUTING

【影响因子】3.401

【Abstract】The latest 6G improvements secured autonomous driving's realism in Intelligent Autonomous Transport Systems (IATS). Despite the IATS's benefits, security remains a significant challenge. Blockchain technology has grown in popularity as a means of implementing safe, dependable, and decentralised independent IATS systems, allowing for more utilisation of legacy IATS infrastructures and resources, which is especially advantageous for crowdsourcing technologies. Blockchain technology can be used to address security concerns in the IATS and to aid in logistics development. In light of the inadequacy of reliance and inattention to rights created by centralised and conventional logistics systems, this paper discusses the creation of a blockchain-based IATS powered by deep learning for secure cargo and vehicle matching (BDL-IATS). The BDL-IATS approach utilises Ethereum as the primary blockchain for storing private data such as order and shipment details. Additionally, the deep belief network (DBN) model is used to select suitable vehicles and goods for transportation. Additionally, the chaotic krill herd technique is used to tune the DBN model's hyperparameters. The performance of the BDL-IATS technique is validated, and the findings are inspected under a variety of conditions. The simulation findings indicated that the BDL-IATS strategy outperformed recent state-of-the-art approaches.

【Keywords】Blockchain; ethereum; intelligent autonomous transport system; security; deep belief network

【发表时间】2023

【收录时间】2022-08-15

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-交通领域