An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments
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 wh...
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MDPI AG
2016-07-01
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Online Access: | http://www.mdpi.com/1099-4300/18/7/274 |
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author | Xiong Luo Ji Liu Dandan Zhang Weiping Wang Yueqin Zhu |
author_facet | Xiong Luo Ji Liu Dandan Zhang Weiping Wang Yueqin Zhu |
author_sort | Xiong Luo |
collection | DOAJ |
description | 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. |
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institution | Directory Open Access Journal |
issn | 1099-4300 |
language | English |
last_indexed | 2024-04-11T13:56:49Z |
publishDate | 2016-07-01 |
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spelling | doaj.art-1ff90fc4eff5416bb2c17e5aebc155df2022-12-22T04:20:14ZengMDPI AGEntropy1099-43002016-07-0118727410.3390/e18070274e18070274An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network EnvironmentsXiong Luo0Ji Liu1Dandan Zhang2Weiping Wang3Yueqin Zhu4School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, ChinaSchool of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, ChinaDevelopment and Research Center, China Geological Survey, Beijing 100037, ChinaWith 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.http://www.mdpi.com/1099-4300/18/7/274kernel least mean square (KLMS)information entropydata predictionlearning |
spellingShingle | Xiong Luo Ji Liu Dandan Zhang Weiping Wang Yueqin Zhu An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments Entropy kernel least mean square (KLMS) information entropy data prediction learning |
title | An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments |
title_full | An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments |
title_fullStr | An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments |
title_full_unstemmed | An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments |
title_short | An Entropy-Based Kernel Learning Scheme toward Efficient Data Prediction in Cloud-Assisted Network Environments |
title_sort | entropy based kernel learning scheme toward efficient data prediction in cloud assisted network environments |
topic | kernel least mean square (KLMS) information entropy data prediction learning |
url | http://www.mdpi.com/1099-4300/18/7/274 |
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