收稿日期: 2017-08-25
修回日期: 2017-10-05
网络出版日期: 2018-05-31
基金资助
国家自然科学基金（No.61402094）；辽宁省自然科学基金（No.201602254）；河北省自然科学基金（No.F2016501076）；教育部中央高校基本科研业务费资助项目基金（No.N172304022）资助
An Autoencoder-Based Data Collection Scheme for Wireless Sensor Networks
Received date: 2017-08-25
Revised date: 2017-10-05
Online published: 2018-05-31
李国瑞, 田丽, 崔浩, 陈浩波 . 一种基于自编码器的无线传感网数据收集方案[J]. 应用科学学报, 2018 , 36(3) : 411 -419 . DOI: 10.3969/j.issn.0255-8297.2018.03.001
Data collection is one of the key operations in wireless sensor networks. In this paper, we propose an energy efcient data collection scheme for wireless sensor networks by using an autoencoder. It includes the model training process and the data collection process. In the model training process, historical dataset is utilized to train the autoencoder with the goal of obtaining a measurement matrix and a reconstruction matrix. In the data collection process, the measurement matrix is utilized to compress the sensed data in a distributed manner and the reconstruction matrix is utilized to reconstruct the surveillant data of the whole sensor network. The experiment results show that the proposed scheme presents higher data compression ratio and higher data reconstruction accuracy as well as faster data reconstruction speed than existed data collection schemes.
[1] Rawat P, Singh K, Chaouchi H, Bonnin J. Wireless sensor networks:a survey on recent developments and potential synergies[J]. The Journal of Supercomputing, 2014, 68(1):1-48.
[2] Campobello G, Segreto A, Serrano S. Data gathering techniques for wireless sensor networks:a comparison[J]. International Journal of Distributed Sensor Networks, 2016, 12(3):1-17.
[3] Monika R, Hemalatha R, Radha S. Energy efcient weighted sampling matrix based CS technique for WSN[C]//Proceedings of the 14th IEEE Sensors Conference, Busan, 2015:1-4.
[4] Lan K C, Wei M Z. A compressibility-based clustering algorithm for hierarchical compressive data gathering[J]. IEEE Sensors Journal, 2017, 17(8):2550-2562.
[5] Wang K, Liu Y, Wang Q, Jing X. Compressed sensing of IR-UWB wireless sensor network data based on two-dimensional measurements[J]. Chinese Journal of Electronics, 2015, 24(3):627-632.
[6] Karakus C, Gurbuz C, Tavli B. Analysis of energy efciency of compressive sensing in wireless sensor networks[J]. IEEE Sensors Journal, 2013, 13(5):1999-2008
[7] Chen W, Rodrigues D, Wassell J. A Frechet mean approach for compressive sensing date acquisition and reconstruction in wireless sensor networks[J]. IEEE Transactions on Wireless Communications, 2012, 11(10):3598-3606.
[8] Tropp J, Gilbert A. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 2007, 53(12):4655-4666.
[9] Blumensath T, Davies M. Iterative hard thresholding for compressed sensing[J]. Applied & Computational Harmonic Analysis, 2008, 27(3):265-274.
[10] Rumelhart D, Hinton G, Williams J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088):533-536.
[11] Luo C, Wu F, Sun J, Chen C W. Compressive data gathering for large-scale wireless sensor networks[C]//Proceedings of the 15th Annual International Conference on Mobile Computing and Networking. New York, 2009:145-456.
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