一个改进的深度强化学习路由技术,用于无碰撞的VANET
Pratima Upadhyay1, Venkatadri Marriboina2, Samta Jain Goyal1
1Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University Gwalior, Gwalior, Madhya-Pradesh, India.
Scientific reports
|December 8, 2023
概括
本研究介绍了一种改进的深度强化学习 (IDRL) 路由方法,用于车辆特设网络 (VANET). IDRL有效地减少了路由复杂性和控制开销,提高了数据传输效率和可靠性.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 网络工程 网络工程
背景情况:
- 车辆特设网络 (VANET) 面临着路由复杂性和高控制开销的挑战.
- 现有的解决方案往往无法将路由优化与开销降低相结合.
研究的目的:
- 为VANETs引入一个改进的深度强化学习 (IDRL) 方法.
- 为了解决路由复杂性,同时最大限度地减少控制开销.
- 优化路线路径,减少动态车辆密度的融合时间.
主要方法:
- 开发了一个IDRL路由技术,利用传输能力和车辆数据.
- 使用车辆到基础设施 (V2I) 通信,通过相邻的车辆进行数据包运输.
- 模拟IDRL方法来评估弹性,可扩展性和效率.
主要成果:
- IDRL方法有效地减少了传输延迟和增强的控制开销.
- 实现了优化的路由路径和缩短的融合时间.
- 通过V2I通信在安全消息传输方面表现出高效.
结论:
- 拟议的IDRL路由方法显著降低了延迟,并增加了数据包交付比率.
- 与现有的路由技术相比,IDRL提高了VANET中的数据可靠性.
- 该方法在管理动态车辆密度和网络开销方面被证明是有效的.
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