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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2016
عنوان انگلیسی مقاله:
Extracting Kernel Dataset from Big Sensory Data in Wireless Sensor Networks
ترجمه فارسی عنوان مقاله:
استخراج مجموعه داده های هسته ای از داده های حسی بزرگ در شبکه های سنسور بی سیم
منبع:
IEEE - This article has been accepted for publication in a future issue of this journal, but has not been fully edited: Content may change prior to final publication: Citation information: DOI 10:1109/TKDE:2016:2645212
نویسنده:
Siyao Cheng, Zhipeng Cai, Jianzhong Li and Hong Gao
چکیده انگلیسی:
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(n3) 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.
Index Terms: Big Sensory Data | Kernel Dataset | Wireless Sensor Networks
قیمت: رایگان
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