Real-time Edge Analytics for Cyber Physical Systems using Compression Rates
Real-time Edge Analytics for Cyber Physical Systems using Compression Rates
复制标题
使用压缩率对网络物理系统进行实时边缘分析
DOI：
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
J. Mccann
中科院分区：
文献类型：
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作者：
Sokratis Kartakis;J. Mccann
在信息物理系统的许多实际应用中，有一种将处理推向边缘的运动。当CPS执行监视和控制时，这一点尤其重要，因为决策和控制消息接收之间的延迟应该最小。然而，CPS受到通常由电池供电的低资源设备的能力的限制。在本文中，我们提出了一种自适应方案，既减少了在边缘存储高采样率数据所需的资源量，同时又进行了初始数据分析。使用我们的智能水数据集，加上其他现实世界CPS应用程序的选择，我们表明我们的算法减少了98%的计算；数据量增长55%；而在运行时只需要11KB的内存（包括压缩算法）。此外，我们还展示了我们的系统支持自调优和自动重新配置，这意味着减少了手动调优，该方案既可以自动应用于任何类型的原始数据，又可以随着传入数据的性质随时间变化而自我优化。
There is a movement in many practical applications of Cyber-Physical Systems to push processing to the edge. This is particularly important were the CPS is carrying out monitoring and control, where the latency between the decision making and control message reception should be minimal. However, CPS are limited by the capabilities of the typically battery powered low resourced devices. In this paper we present a self-adaptive scheme that both reduces the amount of resources required to store high sample rate data at the edge and at the same time carries out initial data analytics. Using out Smart Water datasets, plus a selection from other real world CPS applications, we show that our algorithm reduces computation by 98%; data volumes by 55%; while requiring only 11KB of memory at runtime (including the compression algorithm). In addition we show that our system supports self-tuning and automatic reconfiguration which means that manual tuning is alleviated and the scheme can be both applied to any kind of raw data automatically and is able self-optimize as the nature of the incoming data changes over time.