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Real-time driver identification in IoV: A deep learning and cloud integration approach.

Hassan Muwafaq Gheni1,2, Laith A AbdulRahaim1, Abdallah Abdellatif3

  • 1Electrical Engineering Department, College of Engineering, University of Babylon, Babylon, Iraq.

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Summary
This summary is machine-generated.

This study presents a novel real-time driver identification system for the Internet of Vehicles (IoV) using deep learning and cloud integration. The system achieves high accuracy, enhancing vehicle security and fleet management.

Keywords:
Cloud computingDeep learningDriver behaviourDriver identificationInternet of vehicle

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Automotive Engineering

Background:

  • The Internet of Vehicles (IoV) requires robust systems for operational efficiency and safety.
  • Seamless end-to-end solutions are crucial for managing real-time data in IoV environments.

Purpose of the Study:

  • To introduce an innovative method for real-time driver identification in IoV.
  • To develop an integrated cloud and deep learning solution for IoV data management and driver identification.

Main Methods:

  • Integration of Google Cloud, Thingsboard, and Apache Kafka for data handling.
  • Application of Convolutional Neural Networks (CNN) and multi-head self-attention for driver identification.
  • Validation on 'Security' and 'collected' datasets.

Main Results:

  • Achieved 99.95% accuracy and F1 score, surpassing previous methods.
  • Maintained 96.2% accuracy even with sensor data anomalies.
  • Demonstrated resilience against data anomalies.

Conclusions:

  • The proposed end-to-end IoV system enables accurate driver identification.
  • The system supports optimized fleet management, enhanced vehicle security, and personalized driving experiences.
  • The research highlights potential improvements in road safety and transportation management.