利用联合图模型的传感器网络数据修复方法

doi:10.19665/j.issn1001-2400.2020.01.007

摘要/Abstract

摘要：

为了保证无线传感器网络数据本身的可靠性,以及不会因为数据缺失而导致数据处理过程中的效率降低,提出了一种利用联合图模型的传感器网络数据修复算法。首先基于网络数据的时间域平滑特性和空间域平滑特性建立联合图域模型,然后根据联合图域模型中网络数据的关联特性设计迭代恢复算法,最终实现网络数据恢复的目的。通过实验仿真表明,该方法与图信号模型中基于图全变分最小化算法相比,利用联合图模型的修复算法不仅数据修复精度提高约30%,迭代次数下降约80%。

关键词: 无线传感器网络, 数据修复, 图信号处理, 联合图模型

Abstract:

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.

Key words: wireless sensor networks, data recovery, graph signal processing, joint graph model

中图分类号:

TN911.7

杨杰,蒋俊正. 利用联合图模型的传感器网络数据修复方法[J]. 西安电子科技大学学报, 2020, 47(1): 44-51.

YANG Jie,JIANG Junzheng. Method for data recovery in the sensor network based on the joint graph model[J]. Journal of Xidian University, 2020, 47(1): 44-51.

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参考文献 16

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丢失比例 /%	均方根误差		迭代次数
丢失比例 /%	文中算法	GTVM	文中算法	GTVM
30	1.45	2.12	90.43	187.21
40	1.77	2.55	121.34	322.95
50	2.11	2.96	162.22	666.80
60	2.45	3.37	229.07	1576.04
70	2.83	3.86	361.58	5432.41
80	3.26	4.43	641.79	16895.16
90	3.98	5.38	2019.54	54009.75

丢失比例 /%	均方根误差		迭代次数
丢失比例 /%	文中算法	IHT-GS	文中算法	IHT-GS
30	1.00	0.93	90.55	94989.02
40	1.26	5.28	118.97	100000
50	1.51	12.78	160.00	100000
60	1.81	22.01	231.36	100000
70	2.13	34.06	361.30	100000
80	2.51	48.06	630.21	97659.28
90	3.16	62.22	2727.83	25625.24

丢失比例 /%	均方根误差		迭代次数
丢失比例 /%	文中算法	GTVM	文中算法	GTVM
30	0.47	0.67	70.03	133.99
40	0.58	0.84	91.19	225.85
50	0.71	1.06	131.4	440.07
60	0.86	1.32	194.2	994.79
70	1.05	1.67	301.7	2654.68
80	1.32	2.16	508.0	7316.90
90	1.78	2.95	1426.7	21124.05

丢失比例 /%	均方根误差		迭代次数
丢失比例 /%	文中算法	IHT-GS	文中算法	IHT-GS
30	0.37	0.02	69.35	30634.98
40	0.46	0.27	91.03	77086.53
50	0.58	1.59	121.69	99912.16
60	0.72	4.99	175.50	100000
70	0.90	9.57	264.63	99631.05
80	1.18	13.46	475.67	67299.48
90	1.77	17.04	2397.73	5196.93