Extracting Kernel Dataset from Big Sensory Data in Wireless Sensor Networks

The amount of sensory data manifests an explosive growth due to the increasing popularity of Wireless Sensor Networks (WSNs). The scale of sensory data in many applications has already exceeded several petabytes annually, which is beyond the computation and transmission capabilities of conventional...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering Vol. 29; no. 4; pp. 813 - 827
Main Authors: Cheng, Siyao, Cai, Zhipeng, Li, Jianzhong, Gao, Hong
Format: Journal Article
Language:English
Published: New York IEEE 01.04.2017
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithm design and analysis
Algorithms
Big sensory data
Complexity
Complexity theory
Computer simulation
Correlation
Correlation coefficients
Datasets
Distributed algorithms
Exploding wires
Kernel
kernel dataset
Kernels
Polynomials
Redundancy
Remote sensors
Sensors
Set theory
Wireless sensor networks
ISSN:1041-4347, 1558-2191
Online Access:Get full text
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Summary:The amount of sensory data manifests an explosive growth due to the increasing popularity of Wireless Sensor Networks (WSNs). The scale of sensory data in many applications has already exceeded several petabytes annually, which is beyond the computation and transmission capabilities of conventional WSNs. On the other hand, the information carried by big sensory data has high redundancy because of strong correlation among sensory data. In this paper, we introduce the novel concept of ϵ-Kernel Dataset, which is only a small data subset and can represent the vast information carried by big sensory data with the information loss rate being less than ϵ, where ϵ can be arbitrarily small. We prove that drawing the minimum ϵ-Kernel Dataset is polynomial time solvable and provide a centralized algorithm with O(n 3 ) time complexity. Furthermore, a distributed algorithm with constant complexity O(1) is designed. It is shown that the result returned by the distributed algorithm can satisfy the ϵ requirement with a near optimal size. Furthermore, two distributed algorithms of maintaining the correlation coefficients among sensor nodes are developed. Finally, the extensive real experiment results and simulation results are presented. The results indicate that all the proposed algorithms have high performance in terms of accuracy and energy efficiency.
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ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2016.2645212