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Data Reduction Based on Compression Technique for Big Data in IoT

机译:基于压缩技术的物联网大数据约简

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The data transmitting is very costly in the IoT sensor nodes and it wastes most of the energy. There are many techniques and concepts concerned with save of energy, mostly dedicated to minimize data transmission. Therefore, we can preserve considerable amount of energy with minimizing the data transmissions in networks of IoT sensor. In this research, we suggested a Data Reduction based on Compression Technique (DRCT) which works in the level of IoT sensor nodes. The DRCT includes two compression stages, a lossy SAX Quantization stage that reduces dynamic range of the sensor data readings, after that a lossless LZW compression to compress the output of the lossy quantization. OMNeT++ simulator along with a real sensory data gathered at Intel Lab are used to show the performance of the proposed method. The simulation experiments illustrate that the proposed DRCT technique provides a better performance than the other techniques in the literature.
机译:数据传输在物联网传感器节点中非常昂贵,并且浪费了大部分能量。有许多与节能有关的技术和概念,主要用于最大限度地减少数据传输。因此,通过最小化物联网传感器网络中的数据传输,我们可以节省大量能源。在这项研究中,我们提出了一种基于压缩技术(DRCT)的数据约简方法,该方法适用于IoT传感器节点级别。 DRCT包括两个压缩阶段,一个有损SAX量化阶段,该阶段可减小传感器数据读数的动态范围,然后进行无损LZW压缩,以压缩有损量化的输出。 OMNeT ++仿真器以及在英特尔实验室收集的真实感官数据被用来展示所提出方法的性能。仿真实验表明,所提出的DRCT技术提供了比文献中其他技术更好的性能。

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