Blockchain-Assisted Vehicle and Cargo Matching Using Optimal Fuzzy Restricted Boltzmann Machine in Autonomous Transport System

Abstract

Blockchain (BC) technologies and cutting-edge artificial intelligence (AI) have the capability of reforming transport systems' sustainability, safety, and efficiency. AI-powered solutions allow autonomous vehicles (AVs) to respond to potential hazards, navigate complex environments, make intelligent decisions, and examine real-time data. BC technologies offer secure and decentralized platforms for data management and sharing, fostering trust and cooperation among different stakeholders in the transportation system. An Autonomous Transport System (ATS) for secure vehicle and cargo matching intends to enhance and optimize transportation by using AVs and ensuring the secure transportation of goods. This mechanism combined AV technology, secure matching, and cargo tracking methods to securely and efficiently transport goods from one place to another. In this aspect, this article presents a Blockchain Assisted Vehicle and Cargo Matching using Optimal Fuzzy Restricted Boltzmann Machine (BAVCM-OFRBM) technique for Autonomous Transport System. The major aim of the BAVCM-OFRBM technique lies in the development of an automated vehicle and cargo matching approach using an optimal DL model. In addition, the BAVCM-OFRBM technique makes use of BC technology to secure transportation details such as order and shipment information. Moreover, the BAVCM-OFRBM technique exploits the FRBM model for the selection of appropriate vehicles and cargo for transportation purposes. Finally, the efficacy of the FRBM model can be improved by the use of Ingenious Crow Search Algorithm (ICSA) based hyperparameter tuning process. The simulation results of the BAVCM-OFRBM technique are tested under different aspects. The experimental outcomes emphasized the betterment of the BAVCM-OFRBM method in vehicle and cargo matching process.