机械振动WSNs子带多阶层自适应量化数据压缩方法

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摘要针对资源受限的机械振动无线传感器网络传输大量振动数据时导致的内存空间消耗大及传输效率低等问题，本文提出一种子带多阶层自适应量化数据压缩方法。首先，传感器节点对原始数据进行分块离散余弦变换(Discrete cosine transform, DCT)以确保子带能量集中；然后采用子带多阶层自适应量化方法对分块变换数据进行量化以减少数据失真，提高重构数据精度；最后，为进一步提升数据的压缩性能和效率，对量化数据进行2-Bit过滤游程算术编码处理。将提出的方法与其他数据压缩方法进行对比以验证本文方法的性能，实验结果表明，该方法可以在资源受限的机械振动无线传感器网络节点中获得良好的压缩效果，对提高大量振动数据传输效率有重要意义。

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	汤恒行
	汤宝平
	赵春华
	叶泉兵

关键词 ：机械振动监测, 无线传感器网络, 数据压缩, 子带多阶层自适应

Abstract：Aiming at the problems of large memory consumption and low transmission efficiency caused by the transmission of large amount of vibration data in resource-constrained mechanical vibration wireless sensor networks, this paper proposes a subband multilayer adaptive quantization data compression method. First, the sensor node performs block discrete cosine transform (DCT) for the original data to ensure that the sub-band energy is concentrated; Then the subband multilayer adaptive quantization method is used to quantize the block transformed data to reduce data distortion and improve the accuracy of reconstructed data; Finally, in order to further improve the data compression performance and efficiency, 2-Bit filtering run-length arithmetic coding is performed for the quantized data. The proposed method is compared with other data compression methods to verify the performance of the method in this paper. The experimental results show that the method can obtain good compression effect in the resource-constrained mechanical vibration wireless sensor network node, which is of great significance to improve the transmission efficiency of a large number of vibration data.

Key words： mechanical vibration monitoring wireless sensor networks(WSNs) data compression subband multilayer adaptation

收稿日期: 2022-05-25 出版日期: 2023-05-28

引用本文:

汤恒行，汤宝平，赵春华，叶泉兵. 机械振动WSNs子带多阶层自适应量化数据压缩方法[J]. 振动与冲击, 2023, 42(10): 188-193.
TANG Hengxing,TANG Baoping,ZHAO Chunhua,YE Quanbing. Subband multilayer adaptive quantization data compression method for wireless sensor networks. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(10): 188-193.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I10/188

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