在车辆互联网中优化信息时代,使用易发生错误的通道
Cui Zhang1, Maoxin Ji2, Qiong Wu2
1School of Internet of Things Engineering, Wuxi Institute of Technology, Wuxi 214121, China.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
这项研究通过调整数据提取速率,考虑多普勒位移和队列特征来优化车辆互联网 (IoV) 系统中的信息时代 (AoI). 结果显示,动态速率调整显著降低了AoI,D/M/1模型的表现优于M/M/1.1.
科学领域:
- 通信工程 通信工程
- 信息理论 信息理论
- 网络性能分析 网络性能分析
背景情况:
- 信息时代 (AoI) 对于车辆互联网 (IoV) 系统中的数据新鲜性至关重要.
- 现有的AOI最小化方案往往忽视了队列动态和多普勒移动等流动性影响.
- 车辆的移动性导致网络拓和通道状况波动,使AoI评估复杂化.
研究的目的:
- 调查多普勒位移对易出错的IoV通道数据传输的影响.
- 使用M/M/1和D/M/1排队模型来导出AoI表达式.
- 通过动态调整车辆数据提取速率来优化系统平均AOI.
主要方法:
- 利用M/M/1和D/M/1队列理论来导出信息时代 (AoI) 表达式.
- 开发了一种在线优化算法,用于动态调整车辆数据提取率.
- 在不同的环境条件和车辆速度下模拟系统性能.
主要成果:
- 调整车辆数据提取速率大大降低了系统的平均AOI.
- 分析了由车辆速度引起的多普勒转移对数据传输的影响.
- 在研究的网络场景中,D/M/1排队模型与M/M/1模型相比,显示了较低的AOI.
结论:
- 数据提取速率的动态调整是最小化 IoV 系统 AoI 的有效策略.
- 排队模型的选择 (D/M/1 vs. M/M/1) 影响系统的整体AoI性能.
- 考虑多普勒位移和队列特征对于在移动 IoV 环境中准确的 AoI 评估至关重要.
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