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Related Experiment Videos

Edge Driven Trust Aware Threat Detection for IoT Enabled Intelligent Transportation Systems.

Khulud Salem Alshudukhi1, Mamoona Humayun2, Aala Oqab Alsalem1

  • 1Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakaka 72388, Saudi Arabia.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary

This study introduces a trust-aware, edge-assisted model for secure vehicular networks within intelligent transportation systems (ITS). It enhances reliability and routing performance, addressing challenges like congestion and data privacy in the Internet of Things (IoT) environment.

Keywords:
Internet of Thingsartificial intelligenceedge computingsecurityvehicular networks

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Area of Science:

  • Computer Science
  • Engineering

Background:

  • Intelligent Transportation Systems (ITS) integrate wireless communication and the Internet of Things (IoT) for enhanced road safety and urban mobility.
  • Existing IoT-ITS environments face challenges with network topology changes, leading to congestion, communication holes, and security vulnerabilities.
  • Vehicular networks require secure interactions to prevent data exposure to malicious devices on unpredictable channels.

Purpose of the Study:

  • To propose a novel trust-aware edge-assisted model for securing vehicular networks in IoT-ITS.
  • To enhance the reliability and optimize routing performance within dynamic urban transportation systems.
  • To ensure data privacy, coherence, and tamper-proof computing in the IoT-ITS environment.

Main Methods:

  • Developed a trust-aware edge-assisted model integrating localized computing for global trust management.
  • Incorporated a blockchain ledger for tamper-proof and transparent data computing across IoT-ITS boundaries.
  • Evaluated the model against Graph-Based Trust-Enabled Routing (GBTR) and Bacteria for Aging Optimization Algorithm (BFOA).

Main Results:

  • Achieved significant improvements in network throughput (50% and 62.5%).
  • Reduced end-to-end delay (33.3% and 37.5%) and routing overhead (34% and 38.7%).
  • Demonstrated a substantial decrease in the false positive rate (67.9% and 68.5%) over dynamic network infrastructures.

Conclusions:

  • The proposed trust-aware edge-assisted model significantly enhances security, reliability, and routing efficiency in IoT-ITS.
  • Blockchain integration ensures data integrity and transparency, crucial for secure vehicular communication.
  • The model offers a robust solution for managing dynamic network conditions and mitigating security threats in smart city transportation.