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.

scholarly journals Semitensor Product Compressive Sensing for Big Data Transmission in Wireless Sensor Networks

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Haipeng Peng ◽  
Ye Tian ◽  
Jürgen Kurths
Keyword(s):  
Big Data ◽  
Compressive Sensing ◽  
Data Transmission ◽  
Matrix Multiplication ◽  
Wireless Sensor ◽  
Storage Space ◽  
Secret Key ◽  
Encrypt Data ◽  
The Matrix ◽  
Data Volume

Big data transmission in wireless sensor network (WSN) consumes energy while the node in WSN is energy-limited, and the data transmitted needs to be encrypted resulting from the ease of being eavesdropped in WSN links. Compressive sensing (CS) can encrypt data and reduce the data volume to solve these two problems. However, the nodes in WSNs are not only energy-limited, but also storage and calculation resource-constrained. The traditional CS uses the measurement matrix as the secret key, which consumes a huge storage space. Moreover, the calculation cost of the traditional CS is large. In this paper, semitensor product compressive sensing (STP-CS) is proposed, which reduces the size of the secret key to save the storage space by breaking through the dimension match restriction of the matrix multiplication and decreases the calculation amount to save the calculation resource. Simulation results show that STP-CS encryption can achieve better performances of saving storage and calculation resources compared with the traditional CS encryption.

scholarly journals Tactile Sensor Array with Fiber Bragg Gratings in Quasi-Distributed Sensing

2018 ◽  
Vol 2018 ◽  
pp. 1-8 ◽  
Author(s):  
Marcelo A. Pedroso ◽  
Lucas H. Negri ◽  
Marcos A. Kamizi ◽  
José L. Fabris ◽  
Marcia Muller
Keyword(s):  
Differential Evolution ◽  
Compressive Sensing ◽  
Sensor Array ◽  
Fiber Bragg Gratings ◽  
Tactile Sensor ◽  
Bragg Gratings ◽  
Central Position ◽  
Reconstruction Method ◽  
Sensing System ◽  
Tactile Sensor Array

This work describes the development of a quasi-distributed real-time tactile sensing system with a reduced number of fiber Bragg grating-based sensors and reports its use with a reconstruction method based on differential evolution. The sensing system is comprised of six fiber Bragg gratings encapsulated in silicone elastomer to form a tactile sensor array with total dimensions of 60 × 80 mm, divided into eight sensing cells with dimensions of 20 × 30 mm. Forces applied at the central position of the sensor array resulted in linear response curves for the gratings, highlighting their coupled responses and allowing the application of compressive sensing. The reduced number of sensors regarding the number of sensing cells results in an undetermined inverse problem, solved with a compressive sensing algorithm with the aid of differential evolution method. The system is capable of identifying and quantifying up to four different loads at four different cells with relative errors lower than 10.5% and signal-to-noise ratio better than 12 dB.

scholarly journals An efficient dictionary refinement algorithm for multiple target counting and localization in wireless sensor networks

2017 ◽  
Vol 13 (8) ◽  
pp. 155014771772380
Author(s):  
Baoming Sun ◽  
Yan Guo ◽  
Gengfa Fang ◽  
Eryk Dutkiewicz
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Compressive Sensing ◽  
State Of The Art ◽  
Region Of Interest ◽  
Wireless Sensor ◽  
Multiple Target ◽  
Reconstruction Algorithms ◽  
Simulation Results ◽  
Refinement Algorithm

Many applications provided by wireless sensor networks rely heavily on the location information of the monitored targets. Since the number of targets in the region of interest is limited, localization benefits from compressive sensing, sampling number can be greatly reduced. Despite many compressive sensing–based localization methods proposed, existing solutions are based on the assumption that all targets fall on a sampled and fixed grid, performing poorly when there are targets deviating from the grid. To address such a problem, in this article, we propose a dictionary refinement algorithm where the grid is iteratively adjusted to alleviate the deviation. In each iteration, the representation coefficient and the grid parameters are updated in turn. After several iterations, the measurements can be sparsely represented by the representation coefficient which indicates the number and locations of multiple targets. Extensive simulation results show that the proposed dictionary refinement algorithm achieves more accurate counting and localization compared to the state-of-the-art compressive sensing reconstruction algorithms.

NonConvex Iteratively Reweighted Least Square Optimization in Compressive Sensing

2011 ◽  
Vol 341-342 ◽  
pp. 629-633
Author(s):  
Madhuparna Chakraborty ◽  
Alaka Barik ◽  
Ravinder Nath ◽  
Victor Dutta
Keyword(s):  
Computer Simulation ◽  
Compressive Sensing ◽  
Least Square ◽  
Sparse Signal ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Reconstruction Method ◽  
Least Square Approach ◽  
Simulation Results ◽  
Regularization Strategy

In this paper, we study a method for sparse signal recovery with the help of iteratively reweighted least square approach, which in many situations outperforms other reconstruction method mentioned in literature in a way that comparatively fewer measurements are needed for exact recovery. The algorithm given involves solving a sequence of weighted minimization for nonconvex problems where the weights for the next iteration are determined from the value of current solution. We present a number of experiments demonstrating the performance of the algorithm. The performance of the algorithm is studied via computer simulation for different number of measurements, and degree of sparsity. Also the simulation results show that improvement is achieved by incorporating regularization strategy.

Bayesian Compressive Sensing of Images Based on Effective Blocking

2014 ◽  
Vol 599-601 ◽  
pp. 1411-1415
Author(s):  
Yan Hai Wu ◽  
Meng Xin Ma ◽  
Nan Wu ◽  
Jing Wang
Keyword(s):  
Compressive Sensing ◽  
State Of The Art ◽  
Relevance Vector Machine ◽  
Superior Performance ◽  
Experimental Result ◽  
Sparse Signal ◽  
Point Estimate ◽  
Data Reconstruction ◽  
Reconstruction Method ◽  
Reconstruction Process

The traditional reconstruction method of Compressive Sensing (CS) was mostly depended on L1-norm linear regression model. And here we propose Bayesian Compressive Sensing (BCS) to reconstruct the signal. It provides posterior distribution of the parameter rather than point estimate, so we can get the uncertainty of the estimation to optimize the data reconstruction process adaptively. In this paper, we employ hierarchical form of Laplace prior, and aiming at improving the efficiency of reconstruction, we segment image into blocks, employ various sample rates to compress different kinds of block and utilize relevance vector machine (RVM) to sparse signal in the reconstruction process. At last, we provide experimental result of image, and compare with the state-of-the-art CS algorithms, it demonstrating the superior performance of the proposed approach.

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.

Efficient Routing with Compressive Sensing for Wireless Sensor Network

2017 ◽  
Vol 26 (2) ◽  
pp. 399-412
Author(s):  
Rania Ahmed ◽  
EL-Sayed M. El-Rabaie ◽  
Mohammed Abd-Elnaby ◽  
FathiAbd EL-Samie
Keyword(s):  
Wireless Sensor Network ◽  
Compressive Sensing ◽  
Sensor Network ◽  
Wireless Sensor

scholarly journals Injection-rolling nozzle in an imprint system with continuous injection direct rolling for ultra-thin microstructure guide light plate

2021 ◽  
Vol 13 (4) ◽  
pp. 168781402110112
Author(s):  
Yan Lou ◽  
Kewei Chen ◽  
Xiangwei Zhou ◽  
Yanfeng Feng
Keyword(s):  
Flow Velocity ◽  
Superior Performance ◽  
Light Transmittance ◽  
Velocity Difference ◽  
Orthogonal Experiments ◽  
Width Direction ◽  
Direct Rolling ◽  
Simulation Results ◽  
Continuous Injection ◽  
Better Than

A novel Injection-rolling Nozzle (IRN) in an imprint system with continuous injection direct rolling (CIDR) for ultra-thin microstructure polymer guide light plates was developed to achieve uniform flow velocity and temperature at the width direction of the cavity exit. A novel IRN cavity was designed. There are eight of feature parameters of cavity were optimized by orthogonal experiments and numerical simulation. Results show that the flow velocity at the width direction of the IRN outlet can reach uniformity, which is far better than that of traditional cavity. The smallest flow velocity difference and temperature difference was 0.6 mm/s and 0.24 K, respectively. The superior performance of the IRN was verified through a CIDR experiment. Several 0.35-mm thick, 340-mm wide, and 10-m long microstructural Polymethyl Methacrylate (PMMA) guide light plates were manufactured. The average filling rates of the microgrooves with the aspect ratio 1:3 reached above 93%. The average light transmittance is 88%.

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