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ITS Based on Deep Graph Convolutional Fraud Detection Network Blockchain-Enabled Fog-Cloud

  • Abdullah Lakhan
  • , Mazin Abed Mohammed
  • , Dheyaa Ahmed Ibrahim
  • , Seifedine Kadry
  • , Karrar Hameed Abdulkareem
  • Wenzhou University
  • Dawood University of Engineering & Technology
  • University of Anbar
  • Imam Ja'afar Al-Sadiq University
  • Noroff University College
  • Al-Muthanna University

Research output: Contribution to journalArticlepeer-review

20 Scopus citations

Abstract

The advancement in transport applications increases at the everyday progress in technologies. Therefore, intelligent transport systems (ITS) gain a lot of progress at the different vehicle levels and in the vehicular area network. However, privacy and security at the network level are critical issues for ITS applications in the existing mechanism. In this paper, the study devises the cost-efficient and secure Serverless Blockchain Enable Task Scheduling (SBETS) ITS system and algorithm framework. The main goal is to reduce processing and security blockchian costs for ITS applications in the system. The processing cost minimizes based on the new proposed function-based price model and secures the data by a suggested deep graph convolutional neural network scheme in the network. The simulation results show that SBETS outperformed all existing ITS systems and minimized processing costs by 10% and fraud detection issues by 50% for transport applications.

Original languageEnglish
Pages (from-to)8399-8408
Number of pages10
JournalIEEE Transactions on Intelligent Transportation Systems
Volume24
Issue number8
DOIs
StatePublished - 1 Aug 2023
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 11 - Sustainable Cities and Communities
    SDG 11 Sustainable Cities and Communities

Keywords

  • ITS
  • SBETS
  • blockchain
  • deep graph convolutional fraud detection
  • function
  • mined

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