Understanding the impact of lossy compressions on IoT smart farm analytics
Understanding the impact of lossy compressions on IoT smart farm analytics
复制标题
了解有损压缩对物联网智能农场分析的影响
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
S. Son
中科院分区：
文献类型：
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作者：
Aekyeung Moon;Jaeyoung Kim;Jialing Zhang;Hang Liu;S. Son
随着各种物联网站收集的数据量不断增加，大数据管理和分析成为物联网应用的巨大挑战。尽管大数据可能会受益于数据压缩技术，但压缩可能会减少可忽略不计的数据量，因此不值得付出努力。本文的观点是，只有有损压缩才能释放物联网的压缩威力，因为与对应的压缩（无损压缩）相比，它可以利用时空模式显着减少数据量。然而，有损压缩面临着压缩过多数据从而失去数据保真度的挑战，这可能会影响分析结果的质量。为了了解有损压缩对物联网数据管理和分析的影响，我们评估了基于能量集中重建的农业传感器数据的几种分类算法。具体来说，我们将三种基于变换的有损压缩机制应用于来自物联网气象站的五个现实世界传感器数据。我们的实验结果表明，变换系数的能量集中度与压缩比以及引入的误差量之间存在明显的关系。虽然我们观察到能量集中度越高，压缩率和错误率越低的总体趋势，但我们还观察到，我们评估的数据集和算法对分类精度的影响各不相同。
As the volume of data collected by various IoT stations increases, Big Data management and analytics becomes a huge challenge for IoT applications. Although Big Data can potentially benefit from data compression techniques, the chances are that compression will reduce a negligible amount of data such that it would not worth the effort. The insight of this paper is that only lossy compression can unleash the power of compression to IoT because, compared with its counterpart (lossless one), it can significantly reduce the data volume by taking advantages of spatiotemporal patterns. However, lossy compression faces the challenge of compressing too much data thus losing the data fidelity, which might affect the quality of analytics outcomes. To understand the impact of lossy compression on IoT data management and analytics, we evaluate several classification algorithms on agricultural sensor data reconstructed based on energy concentration. Specifically, we applied three transformation based lossy compression mechanisms to five real-world sensor data from IoT weather stations. Our experimental results indicate that there is a distinctive relationship between energy concentration on the transformed coefficients and compression ratio as well as the amount of error introduced. While we observe a general trend where the higher energy concentration the lower compression and error rates, we also observe that the impact on classification accuracy varies among data sets and algorithms we evaluated.