基于分布式压缩感知的无线传感器网络异常数据处理

doi:10.11896/jsjkx.180901667

计算机科学 ›› 2020, Vol. 47 ›› Issue (1): 276-280.doi: 10.11896/jsjkx.180901667

基于分布式压缩感知的无线传感器网络异常数据处理

侯明星¹,亓慧¹,黄斌科²

(太原师范学院计算机科学与技术系山西晋中030619)¹;
(西安交通大学电子与信息工程学院西安710049)²

收稿日期:2018-09-06 发布日期:2020-01-19
通讯作者: 亓慧(qihui@tynu.edu.cn)
基金资助:
山西省教育厅项目(J2018159);山西省重点研发计划项目(201803D31055);山西省重点研发计划项目(201803D121088)

Data Abnormality Processing in Wireless Sensor Networks Based on Distributed Compressed Sensing

HOU Ming-xing¹,QI Hui¹,HUANG Bin-ke²

(Department of Computer Science and Technology,Taiyuan Normal University,Jinzhong,Shanxi 030619,China)¹;
(School of Electronics and Information Engineering,Xi’an Jiaotong University,Xi’an 710049,China)²

Received:2018-09-06 Published:2020-01-19
About author:HOU Ming-xing,born in 1990,postgraduate,is member of China Computer Federation (CCF).Her main research interests include compressed sensing,wireless sensor network and machine learning;QI Hui,born in 1981,postgraduate,associate professor,postgraduate supervisor,is member of China Computer Fe-deration (CCF).Her main research interests include machine learning and cloud computing.
Supported by:
This work was supported by the Project of Educational Committee of Shanxi Province (J2018159),Key Research and Development Program of Shanxi Province (201803D31055),Key Research and Development Program of Shanxi Province (201803D121088).

摘要/Abstract

摘要： 无线传感器网络的海量数据采集、传输和处理,对传感器节点的处理能力和功耗提出了严峻挑战,而且现实环境中传感器故障或者环境因素的突变会导致部分采集数据异常,而传统的数据处理方法无法对包含异常的数据进行有效的处理。针对上述问题,文中提出了两类无线传感器网络的异常数据模型,以及相应的基于分布式压缩感知的异常数据处理方法。通过协同的多个传感器进行数据压缩采样,当多个传感器采集的数据包含异常成分时,分布式压缩感知技术对数据中相同的正常分量进行一次统一重构,仅对不同的异常分量进行单独重构,从而避免了对相同数据分量的重复处理,提高了对包含异常成分数据处理的效率。另外,分布式压缩感知技术充分利用数据间的相关性,可有效减少传感器网络的数据采集量,加强其对抗异常数据的鲁棒性。对两类异常数据模型的数值仿真结果表明:相比于传统的基于单组测量值的压缩感知技术,基于分布式压缩感知技术的数据处理方法在提高异常数据重构准确率的同时,将采样数据量减少了约33%,证明了该方法的有效性。

关键词: 分布式压缩感知, 联合稀疏, 无线传感器网络, 压缩采样, 异常数据

Abstract: In wireless sensor networks,massive data acquisition,transmission and processing not only pose severe challenges to the processing ability and power consumption of sensors,but also suffer data anomalies frequently due to sensor failures or sudden changes of environmental factors,which cannot be effectively dealt with by traditional data processing methods.Regarding the problems above,this paper proposed two kinds of data abnormality models and corresponding processing method based on distri-buted compressed sensing (DSC) for wireless sensor networks.When the data collected by multiple sensors contains abnormal components,the DCS reconstructs the same normal component of data only once and the different abnormal components individual-ly,which avoids the repeated processing of the same normal component and improves the processing efficiency of the data containing abnormal components.In addition,DCS makes full use of the correlation of data,which can effectively reduce the amount of data acquisition and enhance the robustness against data anomalies.Numerical simulation results of two kinds of data abnormality models show that compared with the traditional compressed sensing based on single set of measurement,the data processing method based on DCS improves the accuracy of abnormal data reconstruction and reduces the amount of data by about 33%,which proves the effectiveness of the proposed method.

Key words: Compressed sampling, Data abnormality, Distributed compressed sensing, Joint sparsity, Wireless sensor networks

中图分类号:

TP393

侯明星,亓慧,黄斌科. 基于分布式压缩感知的无线传感器网络异常数据处理[J]. 计算机科学, 2020, 47(1): 276-280. https://doi.org/10.11896/jsjkx.180901667

HOU Ming-xing,QI Hui,HUANG Bin-ke. Data Abnormality Processing in Wireless Sensor Networks Based on Distributed Compressed Sensing[J]. Computer Science, 2020, 47(1): 276-280. https://doi.org/10.11896/jsjkx.180901667

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基于分布式压缩感知的无线传感器网络异常数据处理

Data Abnormality Processing in Wireless Sensor Networks Based on Distributed Compressed Sensing

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