基于数据预处理的无线气象传感网数据重构模型

doi:10.11772/j.issn.1001-9081.2016.10.2647

计算机应用 ›› 2016, Vol. 36 ›› Issue (10): 2647-2652.DOI: 10.11772/j.issn.1001-9081.2016.10.2647

• 网络与通信 • 下一篇

基于数据预处理的无线气象传感网数据重构模型

王军^1,2, 杨羊¹, 程勇²

1. 南京信息工程大学计算机与软件学院, 南京 210044;
2. 南京信息工程大学信息化建设与管理处, 南京 210044

收稿日期:2016-03-14 修回日期:2016-06-15 出版日期:2016-10-10 发布日期:2016-10-10
通讯作者: 杨羊,E-mail:yyangnuist@foxmail.com
作者简介:王军(1970—),男,安徽铜陵人,教授,CCF会员,主要研究方向:无线传感器网络、大数据;杨羊(1992—),男,江苏仪征人,硕士研究生,主要研究方向:无线传感器网络数据收集;程勇(1980—),男,重庆人,讲师,博士,CCF会员,主要研究方向:无线传感器网络数据收集、大数据。
基金资助:
国家自然科学基金资助项目（61373064，61402236）；江苏省"六大人才高峰"项目（2015-DZXX-015，2013-DZXX-019）；江苏省产学研前瞻性联合研究项目（BY2014007-2）；公益性行业（气象）科研专项（GYHY201106037）；江苏省农业气象重点实验室开放基金资助项目（KYQ1309）。

Data preprocessing based recovery model in wireless meteorological sensor network

WANG Jun^1,2, YANG Yang¹, CHENG Yong²

1. College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China;
2. Department of Information Construction and Management, Nanjing University of Information Science and Technology, Nanjing Jiangsu 210044, China

Received:2016-03-14 Revised:2016-06-15 Online:2016-10-10 Published:2016-10-10
Supported by:
This work is partially supported by the National Natural Science Foundation of China (61373064, 61402236), the Six Talent Peak Project of Jiangsu Province (2015-DZXX-015,2013-DZXX-019), the Prospective Joint Research Project of Jiangsu Province (BY2014007-2), the Public Welfare Industry (Meteorological) Research Project (GYHY2011 06037), the Jiangsu Provincial Key Laboratory of Agricultural Meteorology Fund (KYQ1309).

摘要/Abstract

摘要： 针对无线气象传感网内由于节点数量大、感知数据冗余度高而导致节点通信耗能过高的问题，提出了数据联合稀疏预处理模型，利用监测区域气象要素预报值和各簇头要素值计算出一个全网公共分量并对网内数据进行预处理。将分布式压缩感知应用于簇型传感网中，对各节点感知数据进行压缩观测，在汇聚节点进行数据重构，从根本上降低节点通信量，均衡负载；同时设计了一个基于公共分量异常数据稀疏方法。仿真实验中，相对于单独使用压缩感知，数据联合稀疏预处理模型能够有效利用数据时空相关性提高数据稀疏度，压缩性能提高了25%，重构性能提高46%；同时，异常数据处理方案能够以96%的高概率恢复异常数据。因此，该数据预处理模型能够提高数据重构效率，有效降低网内数据通信量，延长网络寿命。

关键词: 分布式压缩感知, 无线气象传感器网络, 公共分量分析, 联合稀疏, 数据预处理

Abstract: To solve the problem of excessive communication energy consumption caused by large number of sensor nodes and high redundant sensor data in wireless meteorological sensor network, a Data Preprocessing Model based on Joint Sparsity (DPMJS) was proposed. By combining the meteorological forecast value with every cluster head's value in Wireless Sensor Network (WSN), DPMJS was used to compute a common portions to process sensor data. A data collection framework based on distributed compressed sensing was also applied to reduce data transmission and balance energy consumption in cluster network; data measured in common nodes was recovered in sink node, so as to reduce data communication radically. A suitable method to sparse the abnormal data was also designed. In simulation, using DPMJS can enhance the data sparsity by exploiting spatio-temporal correlation efficiently, and improve data recovery rate by 25%; compared with compressed sensing, data recovery rate is improved by 46%; meanwhile, the abnormal data processing can recovery data successfully by high probability of 96%. Experimental results indicate that the proposed data preprocessing model can increase efficiency of data recovery, reduce the amount of transmission significantly, and prolong the network lifetime.

Key words: distributed compressed sensing, wireless meteorological sensor network, common component analysis, joint sparsity, data preprocessing

中图分类号:

TP393.01

王军, 杨羊, 程勇. 基于数据预处理的无线气象传感网数据重构模型[J]. 计算机应用, 2016, 36(10): 2647-2652.

WANG Jun, YANG Yang, CHENG Yong. Data preprocessing based recovery model in wireless meteorological sensor network[J]. Journal of Computer Applications, 2016, 36(10): 2647-2652.

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基于数据预处理的无线气象传感网数据重构模型

Data preprocessing based recovery model in wireless meteorological sensor network

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