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为提高无线传感器网络的感知精度,提出了一种基于提升小波变换和自适应多项式拟合的多模数据压缩算法AMLP(Adaptive Multiple-Modality Data Compression Algorithm Based on Lifting Wavelet and Adaptive Polynomial Fitting)。在给定相关度阈值的前提下,AMLP算法先对数据进行灰色关联聚类,再对类中的相关数据进行自适应的多项式拟合,然后把未拟合的特征数据抽象成一个矩阵,利用提升小波变换去除数据的时间和空间相关性。最后,通过游程编码对数据作进一步压缩。仿真结果表明,AMLP算法能够有效去除不同数据间的冗余信息以及同种数据间的时间和空间冗余信息,提高压缩比,降低网络能耗。与基于小波的自适应多模数据压缩算法AMMC(Adaptive Multiple-Modality Data Compression Algorithm Based on Wavelet)相比,AMLP算法的数据恢复精度大大优于AMMC算法,压缩比和能耗相近。因此,AMLP算法更适用于要求高精度数据的传感器网络应用,如地质灾害监测、医疗和军事领域。
In order to improve the sensing accuracy of wireless sensor networks, a multi-mode data compression algorithm based on lifting wavelet transform and adaptive polynomial fitting AMLP (Adaptive Multiple-Modality Data Compression Algorithm Based on Lifting Wavelet and Adaptive Polynomial Fitting) is proposed. Given the relevancy threshold, AMLP first clusters the data by gray relation, and then adapts the polynomial fitting to the related data in the class, and then abstract the unmatched characteristic data into a matrix, Enhance the time and spatial correlation of wavelet transform to remove data. Finally, the data is further compressed by run-length encoding. Simulation results show that the AMLP algorithm can effectively remove the redundant information between different data and the redundant information of time and space between the same kind of data to improve the compression ratio and reduce the network energy consumption. Compared with AMMP (Adaptive Multiple-Modality Data Compression Algorithm Based on Wavelet), the data recovery accuracy of AMLP algorithm is much better than that of AMMC algorithm, and the compression ratio and energy consumption are similar. Therefore, the AMLP algorithm is more suitable for sensor network applications that require high-precision data, such as geological hazard monitoring, medical and military applications.