基于CS的机载分簇型WSN数据采集方法

周伟; 景博; 黄以锋; 焦晓璇; 胡家兴; 梁威

doi:10.11959/j.issn.1000-436x.2015197

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基于CS的机载分簇型WSN数据采集方法

学术论文 | 更新时间：2024-06-05

- 基于CS的机载分簇型WSN数据采集方法
- CS-based data collection method for airborne clustering WSN
- 通信学报 2015年36卷第5期页码：130-139
- 作者机构：
  
  空军工程大学航空航天工程学院，陕西西安 710038
- 作者简介：
  
  [ "周伟（1984-），男，陕西西安人，空军工程大学博士生，主要研究方向为现代检测与传感网络技术、压缩感知、故障诊断。" ]
  [ "景博（1965-），女，河北邢台人，空军工程大学教授，主要研究方向为故障预测与健康管理、可测试性设计、传感器网络、数据融合。" ]
  [ "黄以锋（1982-），男，湖南衡阳人，空军工程大学讲师，主要研究方向为现代检测与传感网络技术、可测试性设计、故障诊断。" ]
  [ "焦晓璇（1990-），男，山西运城人，空军工程大学博士生，主要研究方向为信息物理融合技术、测试性设计。" ]
  [ "胡家兴（1991-），男，湖南张家界人，空军工程大学硕士生，主要研究方向为压缩感知。" ]
  [ "梁威（1992-），男，河南信阳人，空军工程大学硕士生，主要研究方向为测试性设计、验证与评估。" ]
- 基金信息：
  
  国家自然科学基金资助项目(51201182)
- DOI：10.11959/j.issn.1000-436x.2015197
  中图分类号： TP393
- 网络出版日期：2015-05，
  
  纸质出版日期：2015-05-25
- 稿件说明：
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周伟, 景博, 黄以锋, 等. 基于CS的机载分簇型WSN数据采集方法[J]. 通信学报, 2015,36(5):130-139.

HOUWei Z, INGBo J, UANGYi-feng H, et al. CS-based data collection method for airborne clustering WSN[J]. Journal on communications, 2015, 36(5): 130-139.
周伟, 景博, 黄以锋, 等. 基于CS的机载分簇型WSN数据采集方法[J]. 通信学报, 2015,36(5):130-139. DOI： 10.11959/j.issn.1000-436x.2015197.

HOUWei Z, INGBo J, UANGYi-feng H, et al. CS-based data collection method for airborne clustering WSN[J]. Journal on communications, 2015, 36(5): 130-139. DOI： 10.11959/j.issn.1000-436x.2015197.

摘要

提出一种适用机载分簇型WSN的数据采集方案。该方案一方面采用随机压缩采样的方式，有效地减少了硬件资源受限的簇成员节点的采样数据量，降低了簇成员节点对硬件资源的要求；另一方面，提出一种基于复合混沌—遗传算法的MP重构方法，将混沌理论良好的局部寻优特性与遗传算法强大的全局搜索能力相结合，有效提高了簇头或Sink中信号重构的性能。实验结果表明，该方案在有效减少簇成员节点采样数据量，且采样频率降为原采样频率1/8的基础上，仍能保证10

-7

数量级的重构精度，为实用型WSN的实现提供了有效借鉴。

Abstract

A data acquisition scheme which was suitable for airborne clustering WSN was proposed.On the one hand

this scheme adopts the random compressive sampling could reduce the amount of sampling data of the cluster nodes ef-fectively

and greatly reducing the hardware requirements of the cluster nodes; on the other hand

put forward a MP re-construction method based on composite chaotic-genetic algorithm expressly

which combined the excellent local searching characteristics of chaos theory with the powerful global search ability of genetic algorithm

could improve the signal reconstruction performance of the cluster head or Sink effectively.The experimental results show that

by di

min-ishing the sampling frequency to 1/8 of the original sampling frequency

this random compressive sensing scheme can dramatically reduce the sampling quantity

and the reconstruction precision can reach 10

-7

magnitude.This random com-pressive sensing scheme provides a useful idea for practical WSN.

关键词

Keywords

references

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