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Entropy 2016, 18(7), 274; doi:10.3390/e18070274

An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments

1,2,* , 1,2
,
1,2
,
1,2,* and 3,4
1
School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
2
Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
3
Development and Research Center, China Geological Survey, Beijing 100037, China
4
Key Laboratory of Geological Information Technology, Ministry of Land and Resources, Beijing 100037, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Badong Chen and Jose C. Principe
Received: 13 May 2016 / Revised: 15 July 2016 / Accepted: 18 July 2016 / Published: 22 July 2016
(This article belongs to the Special Issue Information Theoretic Learning)
View Full-Text   |   Download PDF [587 KB, uploaded 22 July 2016]   |  

Abstract

With the recent emergence of wireless sensor networks (WSNs) in the cloud computing environment, it is now possible to monitor and gather physical information via lots of sensor nodes to meet the requirements of cloud services. Generally, those sensor nodes collect data and send data to sink node where end-users can query all the information and achieve cloud applications. Currently, one of the main disadvantages in the sensor nodes is that they are with limited physical performance relating to less memory for storage and less source of power. Therefore, in order to avoid such limitation, it is necessary to develop an efficient data prediction method in WSN. To serve this purpose, by reducing the redundant data transmission between sensor nodes and sink node while maintaining the required acceptable errors, this article proposes an entropy-based learning scheme for data prediction through the use of kernel least mean square (KLMS) algorithm. The proposed scheme called E-KLMS develops a mechanism to maintain the predicted data synchronous at both sides. Specifically, the kernel-based method is able to adjust the coefficients adaptively in accordance with every input, which will achieve a better performance with smaller prediction errors, while employing information entropy to remove these data which may cause relatively large errors. E-KLMS can effectively solve the tradeoff problem between prediction accuracy and computational efforts while greatly simplifying the training structure compared with some other data prediction approaches. What’s more, the kernel-based method and entropy technique could ensure the prediction effect by both improving the accuracy and reducing errors. Experiments with some real data sets have been carried out to validate the efficiency and effectiveness of E-KLMS learning scheme, and the experiment results show advantages of the our method in prediction accuracy and computational time. View Full-Text
Keywords: kernel least mean square (KLMS); information entropy; data prediction; learning kernel least mean square (KLMS); information entropy; data prediction; learning
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Luo, X.; Liu, J.; Zhang, D.; Wang, W.; Zhu, Y. An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments. Entropy 2016, 18, 274.

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