Spatio-Temporal Compressive Sensing-Based Data Gathering in Wireless Sensor Networks

被引：21

作者：

Li, Xiangling ^{[1
]}

Tao, Xiaofeng ^{[1
]}

Chen, Zhuo ^{[2
]}

机构：

[1] Beijing Univ Posts & Telecommun, Natl Engn Lab Mobile Network Technol, Beijing 100876, Peoples R China

[2] CSIRO, Data 61, Marsfield, NSW 2122, Australia

来源：

IEEE WIRELESS COMMUNICATIONS LETTERS | 2018年 / 7卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Compressive sensing; temporal and spatial correlation; data gathering; wireless sensor networks;

D O I：

10.1109/LWC.2017.2764899

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Sensory data in many wireless sensor networks feature spatio-temporal correlations, and compressive sensing (CS) plays an important role in energy-efficient data gathering. In this letter, we design a new CS-based data gathering algorithm, utilizing random sampling and random walks to select sensory data in temporal and spatial domains, respectively. Each measurement is obtained by summing the selected data. A novel sensing matrix is also designed based on the adjacency matrix of an unbalanced expander graph. Simulation shows that our proposed algorithm reduces energy consumption by up to 50.0% compared to the existing algorithms in a daily sea surface temperature measurement scenario.

引用

页码：198 / 201

页数：4

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