scholarly journals Distributed Data Mining Based on Deep Neural Network for Wireless Sensor Network

2015 ◽  
Vol 11 (7) ◽  
pp. 157453 ◽  
Author(s):  
Chunlin Li ◽  
Xiaofu Xie ◽  
Yuejiang Huang ◽  
Hong Wang ◽  
Changxi Niu
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Deep Neural Network ◽  
Wireless Sensor ◽  
Distributed Data Mining ◽  
Distributed Data

scholarly journals Distributed Data Mining in Wireless Sensor Networks

2016 ◽  
Vol 12 (11) ◽  
pp. 68
Author(s):  
Juan Du ◽  
Fenfen Wu
Keyword(s):  
Data Mining ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Rapid Development ◽  
Wireless Sensor ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Data Resource ◽  
Powerful Knowledge

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.

A Framework for Stream Data Mining over Wireless Sensor Network

2014 ◽  
Vol 631-632 ◽  
pp. 523-528
Author(s):  
Yan Qiu Zhang ◽  
Min Tu ◽  
Yuan Xu ◽  
Yu Li
Keyword(s):  
Data Mining ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Intrinsic Property ◽  
Wireless Sensor ◽  
Data Conversion ◽  
Distributed Data ◽  
Stream Data ◽  
Stream Data Mining ◽  
On Line

A wealth of stream data is produced in the application of wireless sensor network (WSN). The knowledge in stream data can be extracted by data mining and it is useful for decision making. However, it is challenging to apply classical data mining methods on the scenario of WSN due to the factors such as limited power supply, on-line mining, data conversion and dynamic topology. This paper proposed a framework for distributed data mining by combining the existing approaches with the intrinsic property of WSN.

Outlier Detection Using Convolutional Neural Network for Wireless Sensor Network

2021 ◽  
Vol 17 (2) ◽  
pp. 0-0
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Deep Learning ◽  
Wireless Sensor Network ◽  
Convolutional Neural Network ◽  
Sensor Network ◽  
Time Series Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Detection Accuracy

Over the recent years, the term deep learning has been considered as one of the primary choice for handling huge amount of data. Having deeper hidden layers, it surpasses classical methods for detection of outlier in wireless sensor network. The Convolutional Neural Network (CNN) is a biologically inspired computational model which is one of the most popular deep learning approaches. It comprises neurons that self-optimize through learning. EEG generally known as Electroencephalography is a tool used for investigation of brain function and EEG signal gives time-series data as output. In this paper, we propose a state-of-the-art technique designed by processing the time-series data generated by the sensor nodes stored in a large dataset into discrete one-second frames and these frames are projected onto a 2D map images. A convolutional neural network (CNN) is then trained to classify these frames. The result improves detection accuracy and encouraging.

A Data Fusion Scheme in Wireless Sensor Network Based on Optimizing Parameters of Neural Network

2021 ◽  
pp. 315-323
Author(s):  
Thi-Kien Dao ◽  
Trong-The Nguyen ◽  
Van-Dinh Vu ◽  
Truong-Giang Ngo
Keyword(s):  
Neural Network ◽  
Wireless Sensor Network ◽  
Data Fusion ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Fusion Scheme
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