Effective two-tier tokenization for intelligent transportation supply chain systems using hybrid optimized query expansion

Abstract

Intelligent Transportation Supply Chain Systems has continued to metamorphose forward-looking supply chain management (SCM). Using data from the Intelligent Transportation Systems and Supply Chain Management Systems on real-time information Transportation services is critical to survival. The new protocol proposes to fulfill this deficiency gap in Intelligent Transportation Systems and Supply Chain Management. This research suggested the two-tier Tokenization for Intelligent Transportation Supply Chain Systems Using a Hybrid optimized query expansion strategy and Smart Contracts to automatically handle the Intelligent Transportation Supply Chain Systems vehicle alignment on live driving. This helps to enhance the vehicle conditions and traffic to enhance overall transportation systems. This Proposed System has three modules to improve the Intelligent Transportation Supply Chain Systems. Firstly, the Intelligent Transportation Supply Chain Systems for cloud resources scheduling of semantic driving with vehicle alignment. It Shows the Intelligent Transportation activities and Supply Chain Systems management based on “Brent’s algorithm.” This encourages the two-tier tokenization. Broadly, the two-tier Tokenization Enabled Smart Contracts show process verification. The formation of Tier 1 is Fungible Tokens based process verification, and Tier 2 is Non-Fungible Tokens based process verification. Also, the Smart Contracts are adaptable to Security concerns in the Intelligent Transportation Supply Chain Systems. It is Forming the “Hybrid Optimized Query Expansion Strategy” for Intelligent Transportation ranking. The simulation result of effective two-tier tokenization for intelligent transportation supply chain systems using hybrid optimized query expansion strategy and smart contracts improves the supply chain management system in planning at 34.5%, optimization at 56.8%, the level of system management at 60.23%, also the analysis 72.3% and finally at the execution stage by 75%.