传感器网络中误差有界的小波数据压缩算法

Haar Wavelet Data Compression Algorithm with Error Bound for Wireless Sensor Networks

无线传感器网络通常能量、带宽有限,难以适应大量数据传输的需求,需要对原始采样数据进行网内近似或聚合.通过设计误差树和解回归方程组,提出了一种无穷范数误差有界的数据压缩方案.该方法可以同时探索传感器数据中的时间相关和多属性间相关.通过一维Haar小波变换来消除单个数据流中的时间相关.若单个传感器节点可以采集多种物理量,即产生多个数据流,则根据相关系数矩阵选择其中的若干个数据流作为基信号,其他数据流借助一个基用线性回归参数来表示.实验结果表明,该算法能够有效地利用传感数据中存在的时间相关和多属性间相关,显著减少了冗余数据.

Wireless sensor networks usually have limited energy and transmission capacity,and they can’t match the transmission of a great deal of data.So,it is necessary to approximate or aggregate raw data sampled by sensors in networks.By designing an error tree and solving the regression equations set,this paper proposes a data compression scheme with infinite norm error bound for wireless sensor networks.The algorithms in the scheme can simultaneously explore the temporal and multiple-streams correlations among the sensory data.The temporal correlation in one stream is captured by the 1D Haar wavelet transform.For multivariate monitoring sensor networks,some streams from one sensor are selected as the bases according to the correlation coefficient matrix,and the other streams from the same sensor node can be expressed with one of these bases using linear regression.Theoretically and experimentally,it is concluded that the proposed algorithms can effectively exploit the temporal and multiple-streams correlations on the same sensor node and achieve significant data reduction.