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

  • 1231: IoT-driven Computer Vision Technology for Smart Transportation Applications
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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%.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, Madanapalle, India

    G. N. Vivekananda

  2. Department of Computing, Sheffield Hallam University, Sheffield, UK

    Muhammad Aslam Jarwar

  3. Department of Medical Instruments Engineering Techniques, Al-Turath University College, Baghdad, 10021, Iraq

    Mustafa Musa Jaber

  4. School of Mechanical Engineering, Lovly Professional University, Phagwara, India

    Chander Prakash

  5. Division of Research & Innovation, Uttaranchal University, Dehradun, Uttarakhand, 248007, India

    Dharam Buddhi

  6. School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India

    Lydia J. Gnanasigamani

  7. Engineering and Technology School, Universidad Internacional de La Rioja – UNIR, Logroño, Spain

    Ivan Sanz-Prieto

Authors
  1. G. N. Vivekananda
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  2. Muhammad Aslam Jarwar
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  3. Mustafa Musa Jaber
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  4. Chander Prakash
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  5. Dharam Buddhi
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  6. Lydia J. Gnanasigamani
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  7. Ivan Sanz-Prieto
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All author contributing is responsible for designing the framework, analyzing the performance, validating the results, and writing the article.

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Correspondence to G. N. Vivekananda.

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Vivekananda, G.N., Jarwar, M.A., Jaber, M.M. et al. Effective two-tier tokenization for intelligent transportation supply chain systems using hybrid optimized query expansion. Multimed Tools Appl (2022). https://doi.org/10.1007/s11042-022-14317-6

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  • DOI: https://doi.org/10.1007/s11042-022-14317-6

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