传感器网络中一种存储有效的小波渐进数据压缩算法

现有的数据压缩算法大多以节能为设计目标,很少顾及到节点有限的存储容量.设计适合传感器网络小波变换的环模型和基于覆盖重叠的分簇模型,消除边界效应.基于此两种网络模型,分别提出存储有效的二维和三维渐进小波数据压缩算法,该算法依据小波函数的支撑长度和簇头的可用存储容量来确定渐进传送的数据单元,具有存储有效性;依据空间相关性来选择渐进传送数据的传感器节点,从而在存储有效的同时又节省网络传输耗能.从存储开销、能量消耗和网络延时等3个方面分析了算法的性能.理论分析和实验结果表明,和一般的数据压缩算法相比,小波渐进压缩算法在耗能相当的情况下,节省了节点的存储容量.

A Wavelet Data Compression Algorithm with Memory-Efficiency for Wireless Sensor Network

Wireless sensor network is becoming an important field in wireless network, and data compression is a key technique. Most existing data compression algorithms for wireless sensor network give only emphasis on reducing energy consumption, not considering the limited memory of sensor nodes. In this paper, a problem of memory-efficient data compression for wireless sensor network based on wavelet technique is addressed. A virtual grid-based ring topology and an overlapping clustering topology are firstly designed. Employing those two topologies to perform wavelet transform, border effect can be eliminated. Then, two dimensional and three dimensional data compression transmission algorithms are proposed. In those algorithms, the progressively transmitting data units are specified according to wavelet function and the memory of each cluster head. So, the needed memory of each cluster head doesn't depend on the size of sensory data. The proposed algorithms select sensor nodes to transmit data to cluster head based on spatial correlation among sensory data, and thus high compression efficiency is obtained. From the view points of memory, energy consumption and delay, the performance of those algorithms is analyzed. Theoretically and experimentally it is shown that the proposed algorithm doesn't consume much more energy compared with the existing ones. More importantly, it is memory-efficient.