边缘云协同作用为人工智能增强的传感器网络数据:一个实时预测维护框架
Kaushik Sathupadi1, Sandesh Achar2, Shinoy Vengaramkode Bhaskaran3
1Google LLC, Sunnyvale, CA 94089, USA.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
本研究引入了一个边缘云混合框架,用于实时预测维护,显著减少传感器网络的延迟,能源使用和带宽需求.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 工业工程 工业工程 工业工程
背景情况:
- 传感器网络产生大量的实时数据量,由于延迟,能量和带宽限制,使传统的预测维护受到压力.
- 现有的仅基于云的框架在工业环境中难以满足高速数据处理的需求.
研究的目的:
- 提出和评估一个边缘云混合框架,以实现高效的实时预测性维护.
- 解决传感器网络数据分析中延迟,能源消耗和带宽的局限性.
主要方法:
- 在边缘设备上实现K-最近邻居 (KNN) 模型,用于实时异常检测.
- 利用云中的长短期内存 (LSTM) 模型进行深入的时间序列故障预测.
- 开发了一个动态的工作负载管理算法,以优化边缘和云之间的资源分配.
主要成果:
- 与仅使用云计算的解决方案相比,实现了35%的延迟降低.
- 显示能源消耗下降了28%.
- 减少了60%的带宽使用.
结论:
- 拟议的边缘云混合框架为实时预测性维护提供了可扩展和高效的解决方案.
- 这种方法非常适合资源有限,数据密集型环境.
- 优化的任务分配提高了运营效率和维护时间表.
关键词:
K-最近的邻居 (KNN)减少带宽的减少带宽.动态的工作负载管理.能源效率是指能效的能源效率.混合边缘-云框架框架延迟优化 延迟优化长时间短期内存 (LSTM) 网络预测性维护是预测性的维护.传感器网络 传感器网络传感器网络传感器网络 传感器网络更多相关视频
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