APPLICATIONS OF NEURAL NETWORK TECHNOLOGIES IN WIRELESS SENSOR NETWORKS

2020 ◽  
pp. 46-51
Author(s):  
A. E. Khaytbaev ◽  
A. M. Eshmuradov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Simulation Model ◽  
Wireless Sensor ◽  
Management Technique ◽  
The Neural Network ◽  
Network Technologies ◽  
Neural Network Technologies

The purpose of the article is to study the possibilities of improving the efficiency of the sensory network management technique, using the neural network method. The presented model of the wireless sensor network takes into account the charging of the environment. The article also tests the hypothesis of the possibility of organizing distributed computing in wireless sensor networks. To achieve this goal, a number of tasks are allocated: review and analysis of existing methods for managing BSS nodes; definition of simulation model components and their properties of neural networks and their features; testing the results of using the developed method. The article explores the major historical insights of the application of the neural network technologies in wireless sensor networks in the following practical fields: engineering, farming, utility communication networks, manufacturing, emergency notification services, oil and gas wells, forest fires prevention equipment systems, etc. The relevant applications for the continuous monitoring of security and safety measures are critically analyzed in the context of the relevancy of specific decisions to be implemented within the system architecture. The study is focused on the modernization of methods of control and management for the wireless sensor networks considering the environmental factors to be allocated using senor systems for data maintenance, including the information on temperature, humidity, motion, radiation, etc. The article contains the relevant and adequate comparative analysis of the updated versions of node control protocols, the components of the simulation model, and the control method based on neural networks to be identified and tested within the practical organizational settings.

scholarly journals A Neural Network-Based Distributed Compressed Sensing Algorithm to Derive Continuous Hyperspectral Images in Wireless Sensor Networks

2022 ◽  
Author(s):  
Md. Sarkar Hasanuzzaman
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
High Resolution ◽  
Super Resolution ◽  
Hyperspectral Images ◽  
Wireless Sensor ◽  
Remote Sensing Images

Abstract Hyperspectral imaging is a versatile and powerful technology for gathering geo-data. Planes and satellites equipped with hyperspectral cameras are currently the leading contenders for large-scale imaging projects. Aiming at the shortcomings of traditional methods for detecting sparse representation of multi-spectral images, this paper proposes wireless sensor networks (WSNs) based single-hyperspectral image super-resolution method based on deep residual convolutional neural networks. We propose a different strategy that involves merging cheaper multispectral sensors to achieve hyperspectral-like spectral resolution while maintaining the WSN's spatial resolution. This method studies and mines the nonlinear relationship between low-resolution remote sensing images and high-resolution remote sensing images, constructs a deep residual convolutional neural network, connects multiple residual blocks in series, and removes some unnecessary modules. For this purpose, a decision support system is used that provides the outcome to the next layer. Finally, this paper, fully explores the similarities between natural images and hyperspectral images, use natural image samples to train convolutional neural networks, and further use migration learning to introduce the trained network model to the super-resolution problem of high-resolution remote sensing images, and solve the lack of training samples problem. A comparison between different algorithms for processing data on datasets collected in situ and via remote sensing is used to evaluate the proposed approach. The experimental results show that the method has good performance and can obtain better super-resolution effects.

scholarly journals CBN-VAE: A Data Compression Model with Efficient Convolutional Structure for Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3445 ◽  
Author(s):  
Jianlin Liu ◽  
Fenxiong Chen ◽  
Jun Yan ◽  
Dianhong Wang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Wireless Sensor ◽  
Reconstruction Accuracy ◽  
Sensing Data ◽  
Proposed Model

Data compression is a useful method to reduce the communication energy consumption in wireless sensor networks (WSNs). Most existing neural network compression methods focus on improving the compression and reconstruction accuracy (i.e., increasing parameters and layers), ignoring the computation consumption of the network and its application ability in WSNs. In contrast, we pay attention to the computation consumption and application of neural networks, and propose an extremely simple and efficient neural network data compression model. The model combines the feature extraction advantages of Convolutional Neural Network (CNN) with the data generation ability of Variational Autoencoder (VAE) and Restricted Boltzmann Machine (RBM), we call it CBN-VAE. In particular, we propose a new efficient convolutional structure: Downsampling-Convolutional RBM (D-CRBM), and use it to replace the standard convolution to reduce parameters and computational consumption. Specifically, we use the VAE model composed of multiple D-CRBM layers to learn the hidden mathematical features of the sensing data, and use this feature to compress and reconstruct the sensing data. We test the performance of the model by using various real-world WSN datasets. Under the same network size, compared with the CNN, the parameters of CBN-VAE model are reduced by 73.88% and the floating-point operations (FLOPs) are reduced by 96.43% with negligible accuracy loss. Compared with the traditional neural networks, the proposed model is more suitable for application on nodes in WSNs. For the Intel Lab temperature data, the average Signal-to-Noise Ratio (SNR) value of the model can reach 32.51 dB, the average reconstruction error value is 0.0678 °C. The node communication energy consumption can be reduced by 95.83%. Compared with the traditional compression methods, the proposed model has better compression and reconstruction accuracy. At the same time, the experimental results show that the model has good fault detection performance and anti-noise ability. When reconstructing data, the model can effectively avoid fault and noise data.

The Effects of ZigBee Position with Different Hidden Layers in Back-Propagation Neural Network

2011 ◽  
Vol 189-193 ◽  
pp. 1761-1767
Author(s):  
Fang Tsung Liu ◽  
Ceweng Erh Weng ◽  
Chien Ming Huang ◽  
Chang Yan Yang ◽  
Huang Chu Huang
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Back Propagation ◽  
Signal Strength ◽  
Back Propagation Neural Network ◽  
Wireless Sensor ◽  
Measured Signal ◽  
The Neural Network ◽  
Hidden Layer

In this paper, the research topic is that the expert experience is established by the size of the measured signal strength of wireless sensor networks and put the strength of the actual collection of historical data into the neural network model. In order to get the minimize error we use the errors to modify the weights and threshold of the neural network links. We compare the differences of hidden layer neural network and the experimental results. We set up a wireless sensor networks environment to collect the measurement values of signal strength (RSSI) and develop an indoor positioning system.

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