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.

scholarly journals Data Compression Based on Stacked RBM-AE Model for Wireless Sensor Networks

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4273 ◽  
Author(s):  
Jianlin Liu ◽  
Fenxiong Chen ◽  
Dianhong Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Compression Ratio ◽  
Sensor Nodes ◽  
Learning Ability ◽  
Wireless Sensor ◽  
Compression Efficiency ◽  
Sensing Data

Data compression is very important in wireless sensor networks (WSNs) with the limited energy of sensor nodes. Data communication results in energy consumption most of the time; the lifetime of sensor nodes is usually prolonged by reducing data transmission and reception. In this paper, we propose a new Stacked RBM Auto-Encoder (Stacked RBM-AE) model to compress sensing data, which is composed of a encode layer and a decode layer. In the encode layer, the sensing data is compressed; and in the decode layer, the sensing data is reconstructed. The encode layer and the decode layer are composed of four standard Restricted Boltzmann Machines (RBMs). We also provide an energy optimization method that can further reduce the energy consumption of the model storage and calculation by pruning the parameters of the model. We test the performance of the model by using the environment data collected by Intel Lab. When the compression ratio of the model is 10, the average Percentage RMS Difference value is 10.04%, and the average temperature reconstruction error value is 0.2815 °C. The node communication energy consumption in WSNs can be reduced by 90%. Compared with the traditional method, the proposed model has better compression efficiency and reconstruction accuracy under the same compression ratio. Our experiment results show that the new neural network model can not only apply to data compression for WSNs, but also have high compression efficiency and good transfer learning ability.

Research on Wireless Sensor Networks Data Fusion Algorithm Based on COA-BP

2014 ◽  
Vol 539 ◽  
pp. 247-250
Author(s):  
Xiao Xiao Liang ◽  
Li Cao ◽  
Chong Gang Wei ◽  
Ying Gao Yue
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Fusion ◽  
Network Lifetime ◽  
Bp Neural Network ◽  
Network Traffic ◽  
Bp Algorithm ◽  
Wireless Sensor

To improve the wireless sensor networks data fusion efficiency and reduce network traffic and the energy consumption of sensor networks, combined with chaos optimization algorithm and BP algorithm designed a chaotic BP hybrid algorithm (COA-BP), and establish a WSNs data fusion model. This model overcomes shortcomings of the traditional BP neural network model. Using the optimized BP neural network to efficiently extract WSN data and fusion the features among a small number of original date, then sends the extracted features date to aggregation nodes, thus enhance the efficiency of data fusion and prolong the network lifetime. Simulation results show that, compared with LEACH algorithm, BP neural network and PSO-BP algorithm, this algorithm can effectively reduce network traffic, reducing 19% of the total energy consumption of nodes and prolong the network lifetime.

scholarly journals Increasing network lifetime using data compression in wireless sensor networks with energy harvesting

2017 ◽  
Vol 13 (1) ◽  
pp. 155014771668968 ◽  
Author(s):  
Sunyong Kim ◽  
Chiwoo Cho ◽  
Kyung-Joon Park ◽  
Hyuk Lim
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Data Compression ◽  
Network Lifetime ◽  
Average Energy ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Entire Network

In wireless sensor networks powered by battery-limited energy harvesting, sensor nodes that have relatively more energy can help other sensor nodes reduce their energy consumption by compressing the sensing data packets in order to consequently extend the network lifetime. In this article, we consider a data compression technique that can shorten the data packet itself to reduce the energies consumed for packet transmission and reception and to eventually increase the entire network lifetime. First, we present an energy consumption model, in which the energy consumption at each sensor node is derived. We then propose a data compression algorithm that determines the compression level at each sensor node to decrease the total energy consumption depending on the average energy level of neighboring sensor nodes while maximizing the lifetime of multihop wireless sensor networks with energy harvesting. Numerical simulations show that the proposed algorithm achieves a reduced average energy consumption while extending the entire network lifetime.

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 Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs

Electronics ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 98
Author(s):  
Rajkumar Singh Rathore ◽  
Suman Sangwan ◽  
Kabita Adhikari ◽  
Rupak Kharel
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Duty Cycle ◽  
Opportunistic Routing ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Echo State Network ◽  
Proposed Model

Minimizing energy consumption is one of the major challenges in wireless sensor networks (WSNs) due to the limited size of batteries and the resource constrained tiny sensor nodes. Energy harvesting in wireless sensor networks (EH-WSNs) is one of the promising solutions to minimize the energy consumption in wireless sensor networks for prolonging the overall network lifetime. However, static energy harvesting in individual sensor nodes is normally limited and unbalanced among the network nodes. In this context, this paper proposes a modified echo state network (MESN) based dynamic duty cycle with optimal opportunistic routing (OOR) for EH-WSNs. The proposed model is used to act as a predictor for finding the expected energy consumption of the next slot in dynamic duty cycle. The model has adapted a whale optimization algorithm (WOA) for optimally selecting the weights of the neurons in the reservoir layer of the echo state network towards minimizing energy consumption at each node as well as at the network level. The adapted WOA enabled energy harvesting model provides stable output from the MESN relying on optimal weight selection in the reservoir layer. The dynamic duty cycle is updated based on energy consumption and optimal threshold energy for transmission and reception at bit level. The proposed OOR scheme uses multiple energy centric parameters for selecting the relay set oriented forwarding paths for each neighbor nodes. The performance analysis of the proposed model in realistic environments attests the benefits in terms of energy centric metrics such as energy consumption, network lifetime, delay, packet delivery ratio and throughput as compared to the state-of-the-art-techniques.

scholarly journals Energy-Efficient Routing Using Fuzzy Neural Network in Wireless Sensor Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Rajesh Kumar Varun ◽  
Rakesh C. Gangwar ◽  
Omprakash Kaiwartya ◽  
Geetika Aggarwal
Keyword(s):  
Neural Network ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Efficient ◽  
Fuzzy Neural Network ◽  
Residual Energy ◽  
Wireless Sensor ◽  
Energy Efficient Routing ◽  
Fuzzy Neural

In wireless sensor networks, energy is a precious resource that should be utilized wisely to improve its life. Uneven distribution of load over sensor devices is also the reason for the depletion of energy that can cause interruptions in network operations as well. For the next generation’s ubiquitous sensor networks, a single artificial intelligence methodology is not able to resolve the issue of energy and load. Therefore, this paper proposes an energy-efficient routing using a fuzzy neural network (ERFN) to minimize the energy consumption while fairly equalizing energy consumption among sensors thus as to prolong the lifetime of the WSN. The algorithm utilizes fuzzy logic and neural network concepts for the intelligent selection of cluster head (CH) that will precisely consume equal energy of the sensors. In this work, fuzzy rules, sets, and membership functions are developed to make decisions regarding next-hop selection based on the total residual energy, link quality, and forward progress towards the sink. The developed algorithm ERFN proofs its efficiency as compared to the state-of-the-art algorithms concerning the number of alive nodes, percentage of dead nodes, average energy decay, and standard deviation of residual energy.

A New Combined Optimization Model for Wireless Sensor Networks

2012 ◽  
Vol 17 (1-2) ◽  
pp. 61-66
Author(s):  
A. Alijani ◽  
K. Ivaz ◽  
S. Mahjoub
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Multiobjective Optimization ◽  
Optimization Model ◽  
Life Time ◽  
Wireless Sensor ◽  
Proposed Model ◽  
New Location

Abstract In this paper we proposed a multiobjective optimization model for wireless sensor networks (WSNs). The proposed model optimized several objectives, simultaneously. Indeed, by starting from a generic configuration we found new location for sensors, that the network have appropriate performance in terms of energy consumption and travelled distance. For the monotony of energy consumption and life time of sensors, the rate of energy consumption in each stage have been associated the previous stage. Through a series of calculations the behavior of the proposed model has been compared with other one-objective models.

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 Performance Analysis of Data Compression Algorithms for Energy Efficient Wireless Sensor Networks

2019 ◽  
Vol 9 (1S) ◽  
pp. 404-408
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Compression ◽  
Energy Saving ◽  
Life Time ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Piecewise Linear Approximation ◽  
Compression Algorithms

Wireless Sensor Networks (WSN), is an intensive area of research which is often used for monitoring, sensing and tracking various environmental conditions. It consists of a number of sensor nodes that are powered with fixed low powered batteries. These batteries cannot be changed often as most of the WSN will be in remote areas. Life time of WSN mainly depends on the energy consumed by the sensor nodes. In order to prolong the networks life time, the energy consumption has to be reduced. Different energy saving schemes has been proposed over the years. Data compression is one among the proposed schemes that can scale down the amount of data transferred between nodes and results in energy saving. In this paper, an attempt is made to analyze the performances of three different data compression algorithms viz. Light Weight Temporal Compression (LTC), Piecewise Linear Approximation with Minimum Number of Line Segments (PLAMLIS) and Univariate Least Absolute Selection and Shrinkage Operator (ULASSO). These algorithms are tested on standard univariate datasets and evaluated using assessment metrics like Mean Square Error (MSE), compression ratio and energy consumption. The results show that the ULASSO algorithm outperforms other algorithms in all three metrics and contributes more towards energy consumption

scholarly journals New method for route efficient energy calculations with mobile-sink for wireless sensor networks

2022 ◽  
Vol 25 (1) ◽  
pp. 365
Author(s):  
Mohammad Khalaf Rahim Al-juaifari ◽  
Jammel Mohammed Ali Mohammed Mona ◽  
Zainab Abd Abbas
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Movement Time ◽  
Path Loss ◽  
Life Time ◽  
Mobile Sink ◽  
Previous Method ◽  
Wireless Sensor ◽  
Proposed Model

<p>Despite proposing a number of algorithms and protocols, especially those related to routing, for the purpose of reducing energy consumption in wireless sensor networks, which is one of the most important issues facing this type of network. In this research paper, energy consumption and cost are calculated taking into account energy consumption and the amount of data transferred to a thousand nodes through specific paths towards the mobile sink. The proposed model simulated by sending various amounts of data with specific path to know the energy consumption of each track and the network life time with 250, 500, and 1000 bits. Cost calculated using various weight for each track of these paths and the coefficient of movement time and path loss factor and others related to the transmission and receiving circuits. And finally, the results compared with a previous method it showed the efficiency of our method used and calculating 1000 nodes with various amount of bits to show the experimental results. Deep learning used to remember each and every path of each position or nearby to avoid calculation cost later.</p>

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