Abstract
With the rapid development and wide application of sensor network technology, consequently a huge volume of data would be continuously generated and collected. In order to process the data and analyze the data more accurate and efficient, the paper proposed a distributed data mining method in wireless sensor networks. Thus Smart Octopus, an open framework for seamlessly integrating sensor network and data mining technology, so that both of the huge amounts of data resource collected in sensor networks and the powerful knowledge discovery capability of data mining could be effectively and efficiently utilized, is discussed in this paper.
Highlights
Rapid advances in wireless communication, microelectronics and embedded systems during the past several decades have enabled the development of low-cost, low-power and multi-functional sensor nodes that are small in size and capable of untethered communicating within short distance
For the problem of most of sensor data estimation methods did not consider the characteristics of sensor data, which may lead to high computational complexity, Li [4] propose a correlation analysis-based estimation framework of sensor data
For the problem of high computational complexity of SVR (Support Vector Regression)–based estimation method, based on the framework, he proposed correlation analysis-based LSSVR (Least Square Support Vector Regression) sensor data estimation method called CALS-SVR (Correlation Analysis-based LS-SVR). He considers the characteristics of the sensor data of wireless sensor networks, and extracts the most correlated sensor variable to be used as the input of modeling and estimation
Summary
Rapid advances in wireless communication, microelectronics and embedded systems during the past several decades have enabled the development of low-cost, low-power and multi-functional sensor nodes that are small in size and capable of untethered communicating within short distance. For the problem of high computational complexity of SVR (Support Vector Regression)–based estimation method, based on the framework, he proposed correlation analysis-based LSSVR (Least Square Support Vector Regression) sensor data estimation method called CALS-SVR (Correlation Analysis-based LS-SVR). In this method, he considers the characteristics of the sensor data of wireless sensor networks, and extracts the most correlated sensor variable to be used as the input of modeling and estimation. The experiments results show that the proposed CALS-SVR has better estimation efficiency and higher estimation accuracy compared to present sensor estimation method based SVR and LS-SVR
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