基于卡尔曼预测与压缩感知的WSN中高能效数据收集方法

周颖; 杨丽花; 杨龙祥; 倪梦

doi:10.11959/j.issn.1000−0801.2019017

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基于卡尔曼预测与压缩感知的WSN中高能效数据收集方法

研究与开发 | 更新时间：2024-06-05

- 基于卡尔曼预测与压缩感知的WSN中高能效数据收集方法
- Energy-efficient data gathering scheme based on Kalman prediction and compressed sensing
- 电信科学 2019年35卷第1期页码：74-80
- 作者机构：
- 作者简介：
  
  [ "周颖（1990− ），女，南京邮电大学博士生，主要研究方向为无线传感器网络、无线通信和物联网等。" ]
  [ "杨丽花（1984− ），女，南京邮电大学副教授，主要研究方向为无线通信、无线传感器网络和网络编码等。" ]
  [ "杨龙祥（1966− ），男，南京邮电大学教授，主要研究方向为无线通信、移动通信系统和物联网。" ]
  [ "倪梦（1993− ），女，南京邮电大学硕士生，主要研究方向为无线传感器网络、移动通信和物联网等。" ]
- 基金信息：
  
  江苏省高等学校自然科学研究项目;The Natural Science Foundation of Jiangsu Higher Education Institutions of China(No.18KJ13510034);第11批中国博士后科学基金资助项目;The 11th Batch of China Postdoctoral Science Fund Special Funding(No.2018T110530);国家自然科学基金资助项目;The National Natural Science Foundation of China(No.61401232);国家自然科学基金资助项目;The National Natural Science Foundation of China(No.61427801);国家自然科学基金资助项目;The National Natural Science Foundation of China(No.61671251);国家自然科学基金资助项目;The National Natural Science Foundation of China(No.61372124);国家自然科学基金资助项目;The National Natural Science Foundation of China(No.61501254);江苏省自然科学基金资助项目;The Natural Science Foundation of Jiangsu Province of China(No.BK20140894)
- DOI：10.11959/j.issn.1000−0801.2019017
  中图分类号： TN929
- 网络出版日期：2019-01，
  
  纸质出版日期：2019-01-20
- 稿件说明：
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周颖, 杨丽花, 杨龙祥, 等. 基于卡尔曼预测与压缩感知的WSN中高能效数据收集方法[J]. 电信科学, 2019,35(1):74-80.

Ying ZHOU, Lihua YANG, Longxiang YANG, et al. Energy-efficient data gathering scheme based on Kalman prediction and compressed sensing[J]. Telecommunications science, 2019, 35(1): 74-80.
周颖, 杨丽花, 杨龙祥, 等. 基于卡尔曼预测与压缩感知的WSN中高能效数据收集方法[J]. 电信科学, 2019,35(1):74-80. DOI： 10.11959/j.issn.1000−0801.2019017.

Ying ZHOU, Lihua YANG, Longxiang YANG, et al. Energy-efficient data gathering scheme based on Kalman prediction and compressed sensing[J]. Telecommunications science, 2019, 35(1): 74-80. DOI： 10.11959/j.issn.1000−0801.2019017.

摘要

提出了一种联合双重卡尔曼滤波与混合压缩感知的数据收集方案。在簇内传输时，簇内成员节点利用卡尔曼滤波预测选择性地向簇头发送数据，以减少数据传输量；同时，簇间传输时，在多跳传输的过程中利用混合压缩感知技术进行数据压缩，提高系统的能效。仿真结果表明，与现有方案相比，新算法能够在保证汇聚节点重构精度的前提下，显著地降低数据收集的能耗。

Abstract

A novel Kalman prediction and compressed sensing based energy-efficient data gathering scheme was proposed. Specially

in the intra-cluster transmission

the cluster members utilized the Kalman prediction to selectively send the data to their cluster heads. In the inter-cluster transmission

the cluster heads leveraged the hybrid compressed sensing to transfer the data to the sink via multi-hop links. Moreover

the communication cost was derived to verify the efficiency of the proposed method. Simulation results show that the proposed method has higher energy efficiency compared with the available schemes

and the sink can obtain measurements with reasonable quality by using the proposed method.

关键词

Keywords

references

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