Title
Critical data points retrieving method for big sensory data in wireless sensor networks
Abstract
With the development and widespread application of wireless sensor networks (WSNs), the amount of sensory data grows sharply and the volumes of some sensory data sets are larger than terabytes, petabytes, or exabytes, which have already exceeded the processing abilities of current WSNs. However, such big sensory data are not necessary for most applications of WSNs, and only a small subset containing critical data points may be enough for analysis, where the critical data points including the extremum and inflection data points of the monitored physical world during given period. Therefore, it is an efficient way to reduce the amount of the big sensory data set by only retrieving the critical data points during sensory data acquisition process. Since most of the traditional sensory data acquisition algorithms were only designed for discrete data and did not support to retrieve critical points from a continuously varying physical world, this paper will study such a problem. In order to solve it, we firstly provided the formal definition of the δ-approximate critical points. Then, a data acquisition algorithm based on numerical analysis and Lagrange interpolation is proposed to acquire the critical points. The extensive theoretical analysis and simulation results are provided, which show that the proposed algorithm can achieve high accuracy for retrieving the δ -approximate critical points from the monitored physical world.
Year
DOI
Venue
2016
10.1186/s13638-015-0505-0
EURASIP J. Wireless Comm. and Networking
Keywords
Field
DocType
Wireless sensor networks, Adaptive sampling, Critical points
Data point,Data mining,Lagrange polynomial,Data set,Petabyte,Adaptive sampling,Terabyte,Computer science,Data acquisition,Real-time computing,Wireless sensor network
Journal
Volume
Issue
ISSN
2016
1
1687-1499
Citations 
PageRank 
References 
4
0.42
19
Authors
4
Name
Order
Citations
PageRank
Tongxin Zhu1213.81
Siyao Cheng243822.59
Zhipeng Cai31928132.81
Jianzhong Li43196304.46