Data Compression Algorithm Based on Sequence Correlation for WSN

ZHAI Shuang; QIAN Zhihong; LIU Xiaohui; SUN Dayang

doi:10.11999/JEIT150280

Volume 38 Issue 3

Mar. 2016

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Article Navigation > Journal of Electronics & Information Technology > 2016 > 38(3): 713-719

Han Tao, Zhu Yue-Fei. Adaptive Spatial Steganography Based on Cannys Edge Detection[J]. Journal of Electronics & Information Technology, 2015, 37(5): 1266-1270. doi: 10.11999/JEIT141121

Citation:

ZHAI Shuang, QIAN Zhihong, LIU Xiaohui, SUN Dayang. Data Compression Algorithm Based on Sequence Correlation for WSN[J]. Journal of Electronics & Information Technology, 2016, 38(3): 713-719. doi: 10.11999/JEIT150280

Han Tao, Zhu Yue-Fei. Adaptive Spatial Steganography Based on Cannys Edge Detection[J]. Journal of Electronics & Information Technology, 2015, 37(5): 1266-1270. doi: 10.11999/JEIT141121

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Data Compression Algorithm Based on Sequence Correlation for WSN

doi: 10.11999/JEIT150280

1.
2.
(College of Communication Engineering, University of Jilin, Changchun 130012, China)

Funds:

The National Natural Science Foundation of China (61371092)

Received Date: 2015-03-09
Rev Recd Date: 2016-01-06
Publish Date: 2016-03-19

Abstract

Abstract

The data has correlations and redundancy in Wireless Sensor Network (WSN). How to reduce effectively the amount of communication data and extend the network life cycle is one of researching hot points. The Two-Step data Compression algorithm based on Sequence Correlation (TSC-SC) for WSN is proposed in this paper. The cluster head and the nodes in clusters perform different compression algorithms for themselves. In order to eliminate the spatial correlation of data and reduce the calculated amount, the cluster head nodes perform the grouping algorithm firstly, then the nodes in clusters perform the classifing compression to eliminate correlation for multi-attribute data, and pass the compression parameters to the cluster head; the cluster head perform the classifing compression again after decompressing the parameters. So the data-redundancy and communication energy consumption is further reduced. A new evaluation model named Network Compression Energy Ratio (NCER) based on energy discrimination is also proposed. The evaluation model realizes comprehensive evaluation of compression algorithms by considering both the basic requirements of compression and calculated energy consumption in the nodes. Simulation results show that TSC-SC algorithm can reduce the compression ratio and compression error effectively; the amount of communication data and energy consumption can achieve a satisfactory level in the network. The algorithm can be estimated directly using NCER.
- Wireless Sensor Network (WSN),
- Data compression,
- Sequence correlation

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References(27)

References

唐亮, 周正, 石磊, 等. 基于能量均衡的无线传感器网络压缩感知算法[J]. 电子与信息学报, 2011, 33(8): 1919-1923. doi: 10.3724/SP.J.1146.2010.01388.

TANG L, ZHOU Z, SHI L, et al. Energy balance based WSN compressive sensing algorithm[J]. Journal of Electronics and Information Technology, 2011, 33(8): 1919-1923. doi: 10.3724 /SP.J.1146.2010.01388.

KIM H W, SEO H S, HONG S H, et al. Modeling of energy-efficient applicable routing algorithm in WSN[J]. International Journal of Digital Content Technology Its Applications, 2010, 4(5): 13-22.

ERRATT N and LIANG Y. Compressed data-stream protocol: an energy-efficient compressed data-stream protocol for wireless sensor networks[J]. IET Communications, 2011, 5(18): 2673-2683. doi: 10.1049/iet- com.2011.0118.

钱志鸿, 王义君. 面向物联网的无线传感器网络综述[J]. 电子与信息学报, 2013, 35(1):?215-227. doi: 10.3724/SP.J.1146. 2012.00876.

QIAN Zhihong and WANG Yijun. Internet of things-oriented wireless sensor networks review[J]. Journal of Electronics Information Technology, 2013, 35(1):?215-227. doi: 10.3724 /SP.J.1146.2012.00876.

徐晓滨, 文成林, 刘荣利. 基于随机集理论的多源信息统一表示与建模方法[J]. 电子学报, 2008, 36(6): 1174-1181.

XU Xiaobin, WEN Chenglin, and LIU Rongli. The unified method of describing and modeling multisource information based on random set theory[J]. Acta Electronica Sinica, 2008, 36(6): 1174-1181.

李阳阳, 王洪波, 张鹏, 等. 基于多属性信息的数据中心间数据传输调度方法[J]. 通信学报, 2012, 33(Z1): 121-131. doi: 10.3969/j.issn.1000-436x.2012.z1.016.

LI Yangyang, WANG Hongbo, ZHANG Peng, et al. Multi-attribute aware scheduling for inter-datacenter bulk transfers[J]. Journal on Communications, 2012, 33(Z1): 121-131. doi: 10.3969/j.issn.1000-436x.2012.z1.016.

CHOU J, PETROVIC D, and RAMCHANDRAN K. A distributed and adaptive signal processing approach to exploiting correlation in sensor networks[J]. Ad Hoc Networks, 2004, 2(4): 387-403. doi: ?10.1016/j.adhoc.2003.09.001.

王雷春, 马传香. 传感器网络中一种基于一元线性回归模型的空时数据压缩算法[J]. 电子与信息学报, 2010, 32(3): 755-758. doi: 10.3724/SP.J.1146.2009.00704.

WANG Leichun and MA Chuanxiang. A one-dimensional linear regression model based spatial and temporal data compression algorithm for wireless senor networks[J]. Journal of Electronics Information Technology, 2010, 32(3): 755-758. doi: 10.3724/SP.J.1146.2009.00704.

蒋鹏, 吴建峰, 吴斌, 等. 基于自适应最优消零的无线传感器网络数据压缩算法研究[J]. 通信学报, 2013, 34(2): 1-7. doi: 10.3969/j.issn.1000-436x.2013.02.001.

JIANG Peng, WU Jianfeng, WU Bin, et al. Data compression method for wireless sensor networks based on adaptive optimal zero suppression[J]. Journal on Communications, 2013, 34(2): 1-7. doi: 10.3969/j.issn.1000-436x.2013.02.001.

杜卓明, 耿国华, 贺毅岳. 一种基于压缩感知的二维几何信号压缩方法[J]. 自动化学报, 2012,?38(11):?1841-1846. doi: 10.3724/SP.J.1004.2012.01841.

DU Zhuoming, GENG Guohua, and HE Yiyue. A 2-D geometric signal compression method based on compressed sensing[J]. Acta Automatic, Sinaca, 2012,?38(11): 1841-1846. doi: 10.3724/SP.J.1004.2012.01841.

陈正宇, 杨庚, 陈蕾, 等. 基于压缩感知的WSNs长生命周期数据收集方法[J]. 电子与信息学报, 2014, 36(10): 2343-2349. doi: 10.3724/SP.J.1146.2013.01787.

CHEN Zhengyu, YANG Geng, CHEN Lei, et al. Data gathering for long network lifetime in WSNs based on compressed sensing[J]. Journal of Electronics Information Technology, 2014, 36(10): 2343-2349. doi: 10.3724/SP.J.1146. 2013.01787.

黄海平, 陈九天, 王汝传, 等. 无线传感器网络中基于数据融合树的压缩感知算法[J]. 电子与信息学报, 2014, 36(10): 2364-2369. doi: 10.3724/SP.J.1146.2013.01621.

HUANG Haiping, CHEN Jiutian, WANG Ruchuan, et al. Compressed sensing algorithm based on data fusion tree in wireless sensor networks[J]. Journal of Electronics Information Technology, 2014, 36(10): 2364-2369. doi: 10.3724/SP.J.1146.2013.01621.

周四望, 李兰. 传感器网络基于DTW的多小波压缩算法[J].通信学报, 2014, 35(8): 86-94. doi: 10.3969/j.issn.1000-436x. 2014.08.012.

ZHOU Siwang and LI Lan. DTW-based multi-wavelet data compression algorithm for wireless sensor networks[J]. Journal on Communications, 2014, 35(8): 86-94. doi: 10.3969/j.issn.1000-436x.2014.08.012.

朱铁军, 林亚平, 周四望, 等. 无线传感器网络中基于小波的自适应多模数据压缩算法[J]. 通信学报, 2009, 30(3): 48-53.

ZHU Tiejun, LIN Yaping, ZHOU Siwang, et al. Adaptive multiple-modalities data compression algorithm using wavelet for wireless sensor networks[J]. Journal on Communications, 2009, 30(3): 48-53.

POLASTRE J, SZEWCZY K R, and CULLER D. Telos: enabling ultra-low power wireless research[C]. Proceedings of the 4th International Symposium on Information Processing in Sensor Networks, California, 2005: 364-369. doi:?10.1109/ IPSN.2005.1440950.

HEINZELMAN W, CHANDRAKASAN A, and BALAKRISHNAN H. Energy-efficient communication protocol for wireless micro sensor networks[C]. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences, Hawaii, 2000: 3005-3014.

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