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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Blockchain-based secure MEC model for VANETs using hybrid networks.

G Vijay Goud1, Rajesh Arunachalam2, Surendra Kumar Shukla3

  • 1Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Thandalam, Chennai, Tamilnadu, 602105, India.

Scientific Reports
|December 16, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a secure Vehicular Ad-hoc Network (VANET) model using deep learning and blockchain. The innovative approach enhances data security and privacy for connected vehicles.

Keywords:
Adaptive and dilated hybrid networkHomomorphic with elliptic curve cryptographyQuality of serviceRandom number updated skill optimization algorithmResidual long short-term memory with gated recurrent unitSecured multi-access edge computingVehicular ad-hoc networks

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Area of Science:

  • Cybersecurity
  • Network Engineering
  • Artificial Intelligence

Background:

  • Vehicular Ad-hoc Networks (VANETs) require robust security and low latency.
  • Multi-Access Edge Computing (MEC) offers proximity-based computation and storage.
  • Integrating MEC with blockchain enhances VANET data processing, security, and privacy.

Purpose of the Study:

  • To develop an innovative, secure VANET model leveraging deep learning, MEC, and blockchain.
  • To enhance data privacy, prevent fraud, and ensure trusted communication in VANETs.
  • To create a blockchain architecture powered by deep learning for VANET safety.

Main Methods:

  • A three-layer network architecture: perception, edge computing, and services.
  • Utilizing an Adaptive and Dilated Hybrid Network (ADHyNet) comprising Res-LSTM and GRU for node authentication.
  • Employing the Random Number Updated Skill Optimization Algorithm (RNU-SOA) for hyperparameter optimization.
  • Implementing Homomorphic Encryption combined with Elliptic Curve Cryptography (HECC) for data encryption.

Main Results:

  • The proposed framework effectively assesses the dependability of vehicle nodes on the blockchain.
  • The ADHyNet model successfully achieves node authentication.
  • The HECC encryption ensures confidential user information is protected against unauthorized access.
  • Simulations demonstrate superior performance in data security compared to existing methods.

Conclusions:

  • The integrated MEC and blockchain model significantly enhances VANET data security and privacy.
  • The deep learning-based blockchain architecture provides a robust solution for VANET safety.
  • The developed system offers improved Quality of Service (QoS) and throughput for users.