Optimal Trained Deep Neural Network for Localization in Wireless Sensor Network

2021 ◽  
Author(s):  
Shivakumar Kagi ◽  
Basavaraj S. Mathapati
Keyword(s):  
Neural Network ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Deep Neural Network ◽  
Wireless Sensor

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

Design of Greenhouse Wireless Sensor Network Control System Based on Fuzzy Neural Network

2011 ◽  
Vol 464 ◽  
pp. 318-321
Author(s):  
Rong Biao Zhang ◽  
Li Hong Wang ◽  
Xian Lin Huang ◽  
Jing Jing Guo
Keyword(s):  
Neural Network ◽  
Control System ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Fuzzy Neural Network ◽  
Network Control ◽  
Environmental Data ◽  
Wireless Sensor ◽  
Wireless Sensor Node ◽  
Fuzzy Neural

This paper proposed a greenhouse control system utilizing wireless sensor network (WSN) to overcome the wiring difficulties and poor mobility in the application of traditional cable-used control systems. Each wireless sensor node in the WSN collects the environmental data of temperature, humidity and CO2 concentration, and transmits the data to the control center via the sink nodes. A fuzzy neural network with three inputs and six outputs was designed to improve the control accuracy. By analyzing the relationship between the mentioned environmental factors above and the actuators of the system, a fuzzy rule was made and combined with the neural network. The simulation results showed that the proposed method could respond in a short time with high accuracy, and had small overshoot as well as good stability.

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