scholarly journals Convex Optimization for Distributed Acoustic Beamforming

2021 ◽  
Author(s):  
◽  
Dayle Raymond Jellyman
Keyword(s):  
Convex Optimization ◽  
Wireless Sensor ◽  
Transmission Power ◽  
Small Decrease ◽  
Noise Performance ◽  
Signal To Noise ◽  
New Model ◽  
Noise Simulation ◽  
Simulation Results ◽  
Distributed Convex Optimization

<p>Beamforming filter optimization can be performed over a distributed wireless sensor network, but the output calculation remains either centralized or linked in time to the weights optimization. We propose a distributed method for calculating the beamformer output which is independent of the filter optimization. The new method trades a small decrease in signal to noise performance for a large decrease in transmission power. Background is given on distributed convex optimization and acoustic beamforming. The new model is described with analysis of its behaviour under independent noise. Simulation results demonstrate the desirable properties of the new model in comparison with centralized output computation.</p>

scholarly journals Convex Optimization for Distributed Acoustic Beamforming

2021 ◽  
Author(s):  
◽  
Dayle Raymond Jellyman
Keyword(s):  
Convex Optimization ◽  
Wireless Sensor ◽  
Transmission Power ◽  
Small Decrease ◽  
Noise Performance ◽  
Signal To Noise ◽  
New Model ◽  
Noise Simulation ◽  
Simulation Results ◽  
Distributed Convex Optimization

<p>Beamforming filter optimization can be performed over a distributed wireless sensor network, but the output calculation remains either centralized or linked in time to the weights optimization. We propose a distributed method for calculating the beamformer output which is independent of the filter optimization. The new method trades a small decrease in signal to noise performance for a large decrease in transmission power. Background is given on distributed convex optimization and acoustic beamforming. The new model is described with analysis of its behaviour under independent noise. Simulation results demonstrate the desirable properties of the new model in comparison with centralized output computation.</p>

A GPS-Less Cell-Based Localization Technique for Wireless Sensor Networks

2012 ◽  
Vol 601 ◽  
pp. 376-382
Author(s):  
Xue Jun Li
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Power Level ◽  
Wireless Sensor ◽  
Transmission Power ◽  
Localization Algorithm ◽  
Localization Technique ◽  
Simulation Results

This paper presents a localization algorithm, namely Circle Based Localization (CBL) for GPS-less wireless sensor networks. CBL works by finding the centroid of intersection of any two circles. Furthermore, we study the effect of power level mismatch among anchors. Simulation results show that CBL can significantly improve the accuracy by 5% while reducing the transmission power of anchors.

A Localization Method for WSN Based on Variable Transmission Power

2013 ◽  
Vol 694-697 ◽  
pp. 1055-1059
Author(s):  
Guang Xiong Huang ◽  
Zhi Long Shan
Keyword(s):  
Wireless Sensor Network ◽  
Wireless Sensor ◽  
Location Information ◽  
Transmission Power ◽  
Localization Algorithm ◽  
Localization Method ◽  
Position Accuracy ◽  
Variable Transmission ◽  
Simulation Results ◽  
Range Free

Localization is an essential technology in the application of wireless sensor network. As a range-free localization algorithm, DV-Hop works well in dense and isotropic networks, but not much in irregular and sparse topologies, especially in the big curvature of shortest path case. In this paper, location information beyond the communication range was obtained by means of variable transmission power of enhanced nodes. Besides, three schemes was proposed to meet the need of different scenarios. Moreover, the simulation results validate that our method can improve position accuracy about 20% and ameliorate the performance of DV-Hop in irregular scenario.

scholarly journals Deep Learning-Based Multiparametric Predictions for IoT

2020 ◽  
Vol 12 (18) ◽  
pp. 7752
Author(s):  
Muhammad Ateeq ◽  
Muhammad Khalil Afzal ◽  
Muhammad Naeem ◽  
Muhammad Shafiq ◽  
Jin-Ghoo Choi
Keyword(s):  
Channel Coding ◽  
Deep Neural Network ◽  
Signal To Noise Ratio ◽  
Wireless Sensor ◽  
Delivery Ratio ◽  
Transmission Power ◽  
Signal To Noise ◽  
Performance Requirement ◽  
Packet Delivery ◽  
Key Indicator

Wireless Sensor Networks (WSNs) and Internet of Things (IoT) often suffer from error-prone links when deployed in resource-constrained industrial environments. Reliability is a critical performance requirement of loss-sensitive applications, and Signal-to-Noise Ratio (SNR) is a key indicator of successful communications. In addition to the improvement of the physical layer through modulation and channel coding, machine learning offers adaptive solutions by configuring various communication parameters dynamically. In this paper, we apply a Deep Neural Network (DNN) to predict SNR and Packet Delivery Ratio (PDR). Analysis results based on a real dataset show that the DNN can predict SNR and PDR at the accuracy of up to 96% and 98%, respectively, even when trained with very small fraction (≤10%) of data. Moreover, a common subset of features turns out to be useful in predicting both SNR and PDR so as to encourage considering both metrics jointly. We may control the transmission power in the dynamic and adaptive manner when we have predictable SNR and PDR, and thus fulfill the reliability requirements with energy conservation. This can help in achieving sustainable design for the communication system.

Solving Bottleneck Problem by Transmission Power Control under Various Compression Assumptions for Wireless Sensor Networks

2014 ◽  
Vol 548-549 ◽  
pp. 1465-1470
Author(s):  
Rei Heng Cheng ◽  
Chi Ming Huang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Power Control ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Transmission Power ◽  
Battery Power ◽  
Simulation Results

In a wireless sensor network, sensor nodes located near the sink will have to bear more communication responsibilities in forwarding the data generated by the nodes located far from the sink. The nodes that are away from the sink communicate with the sink via the nodes that are nearby the sink. Thus the nodes nearby the sink will run out of battery power very soon and create bottleneck for communication. Some researches tried to balance the loading of sensors by adjusting the power range of sensors. However, simply reducing the power range does not always reduce the loading of sensors in the bottleneck zone. In this paper, both of the impacts of the transmission power range and compression gain on energy consumption of a wireless network are analyzed. Some simulation results are given to show the correctness of analysis results.

An Obstacle-resistant Relay Node Placement in Constrained Environment

2019 ◽  
Author(s):  
Abhishek Verma ◽  
Virender Ranga
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Mobile Robots ◽  
Relay Node ◽  
Wireless Sensor ◽  
Relay Node Placement ◽  
Node Placement ◽  
Simulation Results ◽  
Additional Hardware ◽  
Shape And Size

Relay node placement in wireless sensor networks for constrained environment is a critical task due to various unavoidable constraints. One of the most important constraints is unpredictable obstacles. Handling obstacles during relay node placement is complicated because of complexity involved to estimate the shape and size of obstacles. This paper presents an Obstacle-resistant relay node placement strategy (ORRNP). The proposed solution not only handles the obstacles but also estimates best locations for relay node placement in the network. It also does not involve any additional hardware (mobile robots) to estimate node locations thus can significantly reduce the deployment costs. Simulation results show the effectiveness of our proposed approach.

scholarly journals Cooperative Spectrum Sensing Based on Convolutional Neural Networks

2021 ◽  
Vol 11 (10) ◽  
pp. 4440
Author(s):  
Youheng Tan ◽  
Xiaojun Jing
Keyword(s):  
Neural Networks ◽  
Spectrum Sensing ◽  
Convolutional Neural Networks ◽  
Cooperative Spectrum Sensing ◽  
Signal To Noise Ratio ◽  
Majority Voting ◽  
Signal To Noise ◽  
Hidden Terminal ◽  
Simulation Results ◽  
Sensing Performance

Cooperative spectrum sensing (CSS) is an important topic due to its capacity to solve the issue of the hidden terminal. However, the sensing performance of CSS is still poor, especially in low signal-to-noise ratio (SNR) situations. In this paper, convolutional neural networks (CNN) are considered to extract the features of the observed signal and, as a consequence, improve the sensing performance. More specifically, a novel two-dimensional dataset of the received signal is established and three classical CNN (LeNet, AlexNet and VGG-16)-based CSS schemes are trained and analyzed on the proposed dataset. In addition, sensing performance comparisons are made between the proposed CNN-based CSS schemes and the AND, OR, majority voting-based CSS schemes. The simulation results state that the sensing accuracy of the proposed schemes is greatly improved and the network depth helps with this.

scholarly journals Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ming Yin ◽  
Kai Yu ◽  
Zhi Wang
Keyword(s):  
Compressive Sensing ◽  
Sensor Array ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Prototype System ◽  
Reconstruction Method ◽  
Compressed Sampling ◽  
Data Volume ◽  
Simulation Results ◽  
Sparsity Structure

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.

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