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
收藏
页码：198 / 201
页数：4
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