机械振动无线传感网络数据分块无损压缩方法

针对目前机械振动无线传感器网络数据无损压缩方法效率低的问题,提出一种数据分块无损压缩方法,该方法主要由数据分割和数据编码组成。传感器网络节点首先对采集的振动数据进行分块处理,利用子带能量自适应量化方法压缩原始数据,以量化压缩数据完成对原始信号的预测;然后将量化数据还原,计算预测差值矩阵;最后,采用线性预测、自适应零游程编码和Range编码对量化压缩数据和差值矩阵进行编码,进一步去除数据冗余。将提出的数据分块无损压缩方法的压缩性能与其他无损压缩方法进行对比,实验结果表明该方法能在资源受限的无线传感器网络节点上有效实现机械振动信号的无损压缩。

Data block based lossless compression for machine vibration wireless sensor networks

Aiming at the problem of the low performance of current data lossless compression methods in machine vibration wireless sensor networks, a data block based lossless compression method is proposed, which consists of data segmentation and data encoding. Firstly, the collected vibration data is divided into blocks by the wireless sensor node, and the subband energy adaptive quantization method is utilized to compress original data. The prediction of original signal is achieved through the quantized compressed data. Then, the node restores the quantized data and calculates the prediction error matrix. Finally, to further remove the data redundancy, the quantized compressed data and prediction error matrix are encoded by the linear prediction, adaptive zero run length coding and range encoding. The compression performance comparison between the proposed data block based lossless compression method and other lossless compression methods is carried out. Experiment results indicate that the lossless compression of machine vibration signals in the resource constrained wireless sensor network node can be effectively realized with this method.