基于区块链的安全MEC模型用于使用混合网络的VANET
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
概括
本研究介绍了一种使用深度学习和区块链的安全车辆临时网络 (VANET) 模型. 这种创新方法提高了联网汽车的数据安全性和隐私.
科学领域:
- 网络安全 网络安全
- 网络工程 网络工程
- 人工智能的人工智能
背景情况:
- 车载临时网络 (VANET) 需要强大的安全性和低延迟.
- 多访问边缘计算 (MEC) 提供基于近距离的计算和存储.
- 将MEC与区块链集成,可以增强VANET的数据处理,安全和隐私.
研究的目的:
- 开发一个创新的,安全的VANET模型,利用深度学习,MEC和区块链.
- 增强数据隐私,防止欺诈,并确保VANET中可信的通信.
- 创建一个由深度学习为VANET安全提供动力的区块链架构.
主要方法:
- 一个三层网络架构:感知,边缘计算和服务.
- 使用适应和扩展混合网络 (ADHyNet),包括Res-LSTM和GRU用于节点身份验证.
- 使用随机数更新技能优化算法 (RNU-SOA) 进行超参数优化.
- 实现同型加密与圆曲线加密 (HECC) 结合用于数据加密.
主要成果:
- 拟议的框架有效地评估了车辆节点在区块链上的可靠性.
- ADHyNet模型成功实现了节点身份验证.
- 在HECC加密确保机密的用户信息被保护免受未经授权的访问.
- 与现有方法相比,模拟表明数据安全性表现优越.
结论:
- 集成的MEC和区块链模型显著提高了VANET数据安全性和隐私.
- 基于深度学习的区块链架构为VANET安全提供了强大的解决方案.
- 开发的系统为用户提供了更好的服务质量 (QoS) 和吞吐量.
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