Energy Function Analysis and Optimized Computation Based on Hopfield Neural Network for Wireless Sensor Network

2011 ◽  
Vol 10 (6) ◽  
pp. 1208-1214 ◽  
Author(s):  
Lejiang Guo ◽  
Bingwen Wang ◽  
Wei Wang ◽  
Zhuo Liu ◽  
Chao Gao
Keyword(s):  
Neural Network ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Energy Function ◽  
Function Analysis ◽  
Hopfield 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.

Designing Energy-Efficient Topologies for Wireless Sensor Network: Neural Approach

2010 ◽  
Vol 6 (1) ◽  
pp. 216716 ◽  
Author(s):  
Chiranjib Patra ◽  
Anjan Guha Roy ◽  
Samiran Chattopadhyay ◽  
Parama Bhaumik
Keyword(s):  
Neural Network ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Routing Protocols ◽  
Energy Efficient ◽  
Ad Hoc ◽  
Wireless Sensor ◽  
Distributed Environment ◽  
Feature Map ◽  
Battery Power

Preserving energy or battery power of wireless sensor network is of major concern. As such type of network, the sensors are deployed in an ad hoc manner, without any deterministic way. This paper is concerned with applying standard routing protocols into wireless sensor network by using topology modified by neural network which proves to be energy efficient as compared with unmodified topology. Neural network has been proved to be a powerful tool in the distributed environment. Here, to capture the true distributed nature of the Wireless Sensor Network (WSN), neural network's Self-Organizing Feature Map (SOFM) is used.

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