基于自适应霍夫曼和Golomb-Rice混合编码的WSN无损压缩算法

Lossless Compression Algorithm Based on Hybrid Coding of Adaptive Huffman and Golomb-Rice for WSN

针对传统无线传感器网络(WSN)数据压缩算法不能兼顾压缩效率和数据丢失的问题,提出利用自适应Huffman与Golomb-Rice混合编码的快速高效无损自适应压缩算法。将自适应Huffman编码与Golomb-Rice编码相结合,解决可变长和动态性问题,并使用启发式方法估计非负编码参数,通过莱斯映射函数变换拉普拉斯分布误差项,将近似几何分布的非负整数作为熵编码器的输入,利用自适应熵编码独立压缩采样数据块。在Sensor Scope真实环境WSN数据集上的实验结果表明,该算法实现了每个样本4.11位的压缩率,最高可节省70.61%的功率,压缩性能和压缩速率均优于S-LZW,LEC等压缩算法。

Aiming at the problem that traditional Wireless Sensor Network（WSN） data compression algorithms cannot take both compression efficiency and data loss into account, a fast and efficient Lossless Adaptive Compression （LAC） algorithm based on adaptive Huffman coding and Golomb-Rice coding is proposed. Hybrid coding of adaptive Huffman and Golomb-Rice is used to solve the problem of variable length and dynamic. Heuristic method is used to simply estimate non-negative Golomb-Rice coding parameters proposed. A rice mapping function is used to transform the Laplace distribution error term so as to approximate the geometric distribution of nonnegative integers, which are used as the input of entropy encoder. Adaptive entropy coding is used to independently compress sampling data block. Experimental results on real environment WSN dataset from SensorScope show that the proposed algorithm acnieves a compression ratio of 4.11 per sample, and can realize power savings of up to 70.61% . Besides, compression performance and compression rate of the proposed algorithm are better than that of S-LZW,LEC and other compression algorithms.