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在时间敏感的工业物联网中,基于噪音的坚固时钟参数估计和低开头时间同步
Long Tang1, Fangyan Li1, Zichao Yu2
1Guangxi Key Laboratory of Braininspired Computing and Intelligent Chips, School of Electronic and Information Engineering, Guangxi Normal University, Guilin 541001, China.
Entropy (Basel, Switzerland)
|September 27, 2025
概括
本研究为工业物联网 (IIoT) 系统引入了一种新的时间同步方法,通过噪声强大的最大概率估计 (NR-MLE) 算法来降低通信开销并提高时钟参数估计的准确性.
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:
- 时间同步对于工业物联网 (IIoT) 运营至关重要.
- 对于高精度同步和低通讯开销而言,IIoT设备的有限资源带来了挑战.
研究的目的:
- 提出一个单一时间的时间同步方案,以减少IIoT的通信开销.
- 开发一个噪声强大的最大概率估计 (NR-MLE) 算法,用于在随机延迟的情况下准确估计时钟参数.
主要方法:
- 单一时间方案通过接入点 (AP) 收集传感器数据,以尽量减少通信开销.
- 矩阵分解和梯度下降用于消除时间数据.
- 最大概率估计 (MLE) 通过使用无证数据,共同估计时钟斜率和偏移.
主要成果:
- 拟议的方案大大降低了通讯开销,缓解了网络拥堵,并实现了端到端的低延迟.
- 与传统的MLE相比,NR-MLE算法显示出更高的时钟参数估计准确性.
- 该NR-MLE算法显示强大的稳定性对随机延迟噪声不断增加的水平.
结论:
- 开发的时间同步方案有效地减少了IIoT网络中的通信开销.
- 该NR-MLE算法提供准确和强大的时钟同步,对于时间敏感的IIoT应用程序至关重要.
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