深度增强学习启用计算卸载:在车辆边缘云计算网络中实现能源优化和安全意识的新框架
1Faculty of Computing and Information Technology (FCIT), University of Tabuk, Tabuk 47713, Saudi Arabia.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
本研究引入了用于车辆边缘云计算的深度强化学习框架,提高了任务卸载效率. 拟议的系统优化了智能运输系统的负载平衡和节能.
科学领域:
- 智能运输系统 智能运输系统
- 计算机科学 计算机科学
- 网络工程 网络工程
背景情况:
- 车辆边缘云计算 (VECC) 面临着由于工作负载分布不均的网络拥堵和服务延迟的挑战.
- 确保数据安全和优化能源消耗是当前VECC系统中的关键问题.
- 传统的卸载机制难以有效地管理异构的网络参数.
研究的目的:
- 为多层VECC网络开发一个支持深度强化学习的计算卸载框架.
- 解决网络拥堵问题,增强数据安全,优化智能运输系统的能源使用.
- 提高车辆边缘计算的效率和可扩展性.
主要方法:
- 一个动态负载平衡算法,分析RSU负载,通道容量和延迟.
- 部署无人驾驶飞行器 (UAV) 以增加高密度地区的资源.
- 实现先进加密标准 (AES) 以动态密钥生成为数据安全.
- 一个具有上下文意识的边缘缓存策略,以减少冗余的计算和能源开销.
- 制定一个混合整数优化模型和一个基于深度学习的算法,用于接近最佳的卸载解决方案.
主要成果:
- 与现有方法相比,拟议的框架大大降低了能源消耗.
- 经验评估表明,在节能和效率方面,性能优越.
- 该系统有效地平衡路边单位 (RSU) 的工作负载,并动态增加资源.
- 通过使用动态密钥生成的AES加密实现了增强的数据安全性.
结论:
- 深度强化学习框架为智能运输系统提供了一个可持续,安全和可扩展的解决方案.
- 动态负载平衡和无人机增强有效减轻网络拥堵,提高资源利用率.
- 该研究强调了人工智能驱动的方法克服VECC局限性的潜力.
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