Temperature error correction based on BP neural network in meteorological wireless sensor network

2017 ◽  
Vol 23 (4) ◽  
pp. 265 ◽  
Author(s):  
Li Ma ◽  
Baowei Wang ◽  
Shuangshuang Yan ◽  
Xiaodu Gu
Keyword(s):  
Neural Network ◽  
Wireless Sensor Network ◽  
Error Correction ◽  
Sensor Network ◽  
Bp Neural Network ◽  
Wireless Sensor ◽  
Temperature Error

scholarly journals Air Temperature Error Correction Based on Solar Radiation in an Economical Meteorological Wireless Sensor Network

Sensors ◽  
2015 ◽  
Vol 15 (8) ◽  
pp. 18114-18139 ◽  
Author(s):  
Xingming Sun ◽  
Shuangshuang Yan ◽  
Baowei Wang ◽  
Li Xia ◽  
Qi Liu ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Solar Radiation ◽  
Error Correction ◽  
Sensor Network ◽  
Air Temperature ◽  
Wireless Sensor ◽  
Temperature Error

Design and Development of Detection Node in Wireless Sensor Network Based on Neural Network

2014 ◽  
Vol 1022 ◽  
pp. 292-295 ◽  
Author(s):  
Shu Min Duan
Keyword(s):  
Neural Network ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Bp Neural Network ◽  
Wireless Sensor ◽  
Design And Development ◽  
Network Nodes ◽  
Feed Forward Network ◽  
Input Layer ◽  
Hidden Layer

Multilayer BP neural network is a one-way transmission of feed forward network and it has three or more than three layers of neural network, including input layer, hidden layer and output layer. Wireless sensor network is composed in certain region has a plurality of wireless communication, sensing, data processing capabilities of network nodes. The paper presents design and development of detection node in wireless sensor network based on neural network. The simulation results prove the validity of the adopted BP neural network to build wireless sensor network node.

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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