Method for data recovery in the sensor network based on the joint graph model

被引：0
|
作者：
Yang J. [1 ]
Jiang J. [1 ,2 ]
机构：
[1] School of Information and Communication, Guilin Univ. of Electronic Technology, Guilin
[2] Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing, Guilin Univ. of Electronic Technology, Guilin
来源：
Xi'an Dianzi Keji Daxue Xuebao/Journal of Xidian University | 2020年 / 47卷 / 01期
关键词：
Data recovery; Graph signal processing; Joint graph model; Wireless sensor networks;
D O I：
10.19665/j.issn1001-2400.2020.01.007
中图分类号：
学科分类号：
摘要：
In order to ensure that the sensor network data are reliable, and that the efficiency of data processing is not reduced due to the lack of network data, a method for data recovery in the sensor network based on the joint graph model is proposed. First, this paper establishes a joint graph domain model based on the smoothness of network data in the time-domain and spatial-domain, and then an iterative recovery method is proposed to recover the network data, which is based on the association characteristics of network data in the joint graph domain model. Experimental simulation shows that compared with the recovery method based on graph total variation minimization in the graph signal model, the method of data recovery based on the joint graph model improves not only by about thirty percent of the data recovery accuracy, but also by about eighty percent of the iteration efficiency. © 2020, The Editorial Board of Journal of Xidian University. All right reserved.
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页码：44 / 51
页数：7
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共 14 条
  • [1] Lu Y., Zhou J., Wan L., Improved Method for 2D Target Coverage in Wireless Sensor Networks, Journal of Xidian University, 46, 2, pp. 101-106, (2019)
  • [2] Bao T., Huang Z., Li D., Data Loss and Reconstruction for Wireless Environmental Sensor Networks, Proceedings of the 20173rd IEEE International Conference on Big Data Security on Cloud, pp. 48-52, (2017)
  • [3] Li B., Gao F., Liu X., Et al., Improved Distributed Compressed Sensing for Smooth Signals in Wireless Sensor Networks, Proceedings of the 2016 International Conference on Computer, Information and Telecommunication Systems, (2016)
  • [4] Ling Q., Tian Z., Collaborative Sparse Signal Recovery in Hierarchical Wireless Sensor Networks, Proceedings of the 20093rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, pp. 125-128, (2009)
  • [5] Ortega A., Frossard P., Kovacevic J., Et al., Graph Signal Processing: Overview, Challenges, and Applications, Proceedings of the IEEE, 106, 5, pp. 808-828, (2018)
  • [6] Chen S., Sandryhaila A., Moura J.M.F., Et al., Signal Recovery on Graphs: Variation Minimization, IEEE Transactions on Signal Processing, 63, 17, pp. 4609-4624, (2015)
  • [7] Chen S., Sandryhaila A., Kovacevic J., Distributed Algorithm for Graph Signal Inpainting, Proceedings of the 2015 IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3731-3735, (2015)
  • [8] Yang L., You K., Guo W., Graph Diffusion Operator Based Weighted Reconstruction Strategy for Band-limited Graph Signals, Journal of Electronics and Information Technology, 39, 12, pp. 2937-2944, (2017)
  • [9] Tseng C.C., Lee S.L., Su R.H., A Missing Temperature Data Estimation Method Using Graph Fourier Transform, Proceedings of the 2017 IEEE International Conference on Consumer Electronics, pp. 87-88, (2017)
  • [10] Tseng C.C., Lee S.L., A Missing Data Recovery Method of Sparse Graph Signal in GFT Domain, Proceedings of the 2018 IEEE International Conference on Consumer Electronics, (2018)
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