PC3: Principal Component-based Context Compression Improving energy efficiency in wireless sensor networks

被引：11

作者：

Anagnostopoulos, Christos ^{[1
]}

Hadjiefthymiades, Stathes ^{[2
]}

Georgas, Panagiotis ^{[2
]}

机构：

[1] Ionian Univ, Dept Informat, Corfu 49100, Greece

[2] Univ Athens, Dept Informat & Telecommun, Athens 15784, Greece

来源：

JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING | 2012年 / 72卷 / 02期

关键词：

Wireless sensor network; Context compression; Energy efficiency; Principal component analysis; Extrapolation; DATA FUSION;

D O I：

10.1016/j.jpdc.2011.10.001

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We focus on energy efficiency, which guarantees the operation of a Wireless Sensor Network for long. We propose a context compression model that works in an orthogonal fashion. We first reduce the dimensions of multivariate contextual information. This is achieved through the Principal Component Analysis (PCA), which determines the statistical dependencies between the different contextual components. We then suppress the transmission of the determined principal components through an extrapolation scheme that exploits the properties of each individual component. Our findings are quite promising for the broader domain of WSN-based application engineering and context awareness. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：155 / 170

页数：16

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