传感器网络基于DTW的多小波压缩算法

提出传感器网络环境下基于DTW的多小波数据压缩算法。首先研究汇聚节点中异步数据点—点对的对应关系，设计迭代算法求出具有最大相关性的DTW弯曲路径。接着提出最佳匹配点选择算法，通过DTW弯曲路径中一对一数据点—点对来预测异步数据向量间的函数关系，获取最佳匹配点，得到具有最大相关性的传感数据矩阵。然后设计多小波变换，利用传感数据矩阵的相关性来压缩数据，同时解决数据矩阵的行列不对称问题。实验结果表明，所提出的算法在能量聚集比、重构精度和运行时间等压缩性能指标上优于经典的分布式小波压缩算法。

DTW-based multi-wavelet data compressionalgorithm for wireless sensor networks

A data compression algorithm for wireless sensor networks based on DTW and multi-wavelet transform is proposed. Firstly, the correlation and warping path of asynchronous data point pairs is introduced, and then an iterative algorithm for solving DTW warping path with maximal similarity is designed. Secondly, a best match point algorithm is presented, which employs prediction to estimate the relationship of synchronous data vectors and then find out the best match points. A sensory data matrix with maximal correlation is thus obtained. Afterwards, a multi-wavelet transform is introduced, which is capable of utilizing the spatial correlation of sensory data matrix and solving the row-column asymmetry problem. Experiment results show that proposed method has higher energy concentration ratio, better reconstruction accuracy and lower running time compared to the traditional distributed wavelet compression algorithm.