基于强化学习的路线优化模型,以提高车辆互联网中的能源效率
Quadeer Hussain1,2, Ahmad Shukri Mohd Noor2, Muhammad Mukhtar Qureshi2,3
1Faculty of Information Technology, Beijing University of Technology, Beijing, 100024, China.
Scientific reports
|January 24, 2025
概括
强化学习模型OptiE2ERL优化了汽车互联网 (IoV) 中的能源效率和路由. 它提高了网络寿命和性能,优于现有的连接车辆通信模型.
科学领域:
- * 计算机科学 计算机科学
- * 电气工程 电气工程
- * * 运输系统 运输系统
背景情况:
- * 汽车互联网 (IoV) 提供先进的交通控制,安全和信息娱乐,但在数据保护,连接和能源效率方面面临挑战.
- * 在 IoV 环境中,数据传输,负载分配和决策所需的能源消耗需要优化解决方案.
研究的目的:
- * 提出 OptiE2ERL,一种基于强化学习 (RL) 的模型,用于优化 IoV 网络中的能源效率和路由.
- * 解决关键的 IoV 挑战,包括能源消耗,通信开销和网络寿命.
主要方法:
- * 开发了OptiE2ERL,使用奖励矩阵和贝尔曼方程来确定最佳路径.
- * 包含的关键参数:剩余能量水平 (REL),带宽和干扰水平 (BIL),移动模式 (MP),交通状况 (TC) 和网络拓安排 (NTA).
- *使用NS2和Python进行了广泛的模拟.
主要成果:
- * 在性能指标上,OptiE2ERL的表现明显优于LEACH,PEGASIS和EER-RL.
- * 证明了网络寿命的延长,延迟了第一个死节点的发生,并保持了更高的剩余能量.
- * 在各种模拟场景中提高网络可扩展性和稳定性.
结论:
- * OptiE2ERL为动态IoV环境提供了强大且节能的路由解决方案.
- * 该模型有效地管理通讯开销,并改善整体网络性能.
- * 这项研究为联网汽车网络的能源优化提供了一种优越的方法.
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