Paper Info
Title
Energy-Efficient and Smoothing-Sensitive Curve Recovery of Sensing Physical World.
Abstract
In recent years, sensing networks are widely used in the application of real-time monitoring. The change process of physical word is smoothing and continuous, but the sensing devices can only obtain the discrete data points. It is likely to lose the key points and distort the true curve if the discrete points are used simply to describe the physical world. Therefore, how to recover the approximate curve of physical world becomes a problem to be solved urgently. Based on this, an energy-efficient and smoothing-sensitive high-precision curve recovery algorithm for the sensing networks is proposed. Firstly, we recover the curve of physical world based on the existing physical-world-aware data acquisition algorithms preliminarily. And then a curve smoothing algorithm is proposed in order to acquire more key points (the inflexions are mainly considered in this paper) information which helps users better understand the change process of monitored physical world intuitively. Secondly, we propose an energy-efficient data source selection algorithm with residual energy of each data source and spatial correlation under consideration simultaneously. We select part of data sources to transmit data, maximize the lifetime of sensing network and minimize the error between the approximate curve and physical world. Finally, the effectiveness of our algorithms is verified by abundant experiments using both real and simulated data.
Year
DOI
Venue
2015
10.1007/978-3-319-22047-5_25
Lecture Notes in Computer Science
Keywords
Field
DocType
Sensing network,Smoothing-sensitive curve recovery,Data source selection
Data source,Data point,Data mining,Discrete points,Spatial correlation,Computer science,Efficient energy use,Selection algorithm,Data acquisition,Smoothing
Conference
Volume
ISSN
Citations 
9196
0302-9743
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Qian Ma111.05
Yu Gu220134.98
Tiancheng Zhang3227.91
Fangfang Li4143.59
Ge YU51313175.88