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Related Experiment Videos

Edge Computing and Blockchain for Quick Fake News Detection in IoV.

Yonggang Xiao1, Yanbing Liu1, Tun Li1

  • 1School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.

Sensors (Basel, Switzerland)
|August 9, 2020
PubMed
Summary

Quick Fake News Detection (QcFND) framework combats misinformation in the Internet of Vehicles (IoV). It uses edge computing, blockchain, and Bayesian networks to ensure timely and reliable traffic information for improved road safety.

Keywords:
Bayesian networksInternet of vehiclesedge computingfake news detectionpermissioned blockchain

Related Experiment Videos

Area of Science:

  • Network computing
  • Information security
  • Intelligent transportation systems

Background:

  • Dissemination of false messages in the Internet of Vehicles (IoV) negatively impacts road safety and traffic efficiency.
  • Timeliness is critical for detecting fake news in IoV environments.
  • Existing solutions may not adequately address the speed and accuracy requirements for fake news detection in IoV.

Purpose of the Study:

  • To propose a novel network computing framework, Quick Fake News Detection (QcFND), for rapid and accurate detection of false messages in IoV.
  • To leverage Software-Defined Networking (SDN), edge computing, and blockchain technologies to enhance the efficiency and reliability of fake news detection.
  • To utilize Bayesian networks for inferring the trustworthiness of traffic reports.

Main Methods:

  • The proposed QcFND framework employs a two-tier architecture: edge and vehicles.
  • Edge tier utilizes Software-Defined Road Side Units (SDRSUs) hosting SDN controllers for load balancing and blockchain servers for report management.
  • Bayesian networks are used to infer the probability of traffic events and assess the credibility of vehicle-submitted reports.

Main Results:

  • QcFND framework demonstrates effective fake news detection in IoV environments.
  • The integration of SDN, edge computing, and blockchain ensures efficient load balancing and secure report handling.
  • Experimental results show that QcFND achieves good performance compared to existing solutions.

Conclusions:

  • The QcFND framework provides a robust solution for timely fake news detection in IoV.
  • The proposed architecture enhances road safety and traffic efficiency by ensuring the reliability of information.
  • The combination of advanced technologies offers a promising direction for securing IoV communications.