In marine wireless sensor networks(MWSNs),an appropriate routing protocol is the key to the collaborative collection and efficient transmission of massive data.However,designing an appropriate routing protocol under t...In marine wireless sensor networks(MWSNs),an appropriate routing protocol is the key to the collaborative collection and efficient transmission of massive data.However,designing an appropriate routing protocol under the condition of sparse marine node deployment,highly dynamic network topology,and limited node energy is complicated.Moreover,the absence of continuous endto-end connection introduces further difficulties in the design of routing protocols.In this case,we present a novel energy-efficient opportunistic routing(Novel Energy-Efficient Opportunistic Routing,NEOR)protocol for MWSNs that is based on compressed sensing and power control.First,a lightweight time-series prediction method-weighted moving average method is proposed to predict the packet advancement value such that the number of location information that is exchanged among a node and its neighbor nodes can be minimized.Second,an adaptive power control mechanism is presented to determine the optimal transmitting power and candidate nodeset on the basis of node mobility,packet advancement,communication link quality,and remaining node energy.Subsequently,a timer-based scheduling algorithm is utilized to coordinate packet forwarding to avoid packet conflict.Furthermore,we introduce the compressed sensing theory to compress perceptual data at source nodes and reconstruct the original data at sink nodes.Therefore,energy consumption in the MWSNs is greatly reduced due to the decrease in the amount of data perception and transmission.Numerical simulation experiments are carried out in a wide range of marine scenarios to verify the superiority of our approach over selected benchmark algorithms.展开更多
An energy-saving algorithm for wireless sensor networks based on network coding and compressed sensing(CS-NCES)is proposed in this paper.Along with considering the correlations of data spatial and temporal,the algorit...An energy-saving algorithm for wireless sensor networks based on network coding and compressed sensing(CS-NCES)is proposed in this paper.Along with considering the correlations of data spatial and temporal,the algorithm utilizes the similarities between the encoding matrix of network coding and the measurement matrix of compressed sensing.The source node firstly encodes the data,then compresses the coding data by cot-npressed sensing over finite fields.Compared with the network coding scheme,simulation results show that CS-NCES reduces the energy consumption about 25.30/0-34.50/0 and improves the efficiency of data reconstruction about 1.56%-5.98%.The proposed algorithm can not only enhance the usability of network coding in wireless sensor networks,but also improve the network performance.展开更多
In order to achieve the acoustic signal distributed acquisition of stored grain pests, a novel acoustic signal acquisition system was presented based on the wireless sensor networks. And the system architecture, hardw...In order to achieve the acoustic signal distributed acquisition of stored grain pests, a novel acoustic signal acquisition system was presented based on the wireless sensor networks. And the system architecture, hardware configuration, and software were introduced in detail. Considering bandwidth limitation of wireless sensor networks, random sampling algorithm based on the compressed sensing theory was proposed. The developed acoustic signal acquisition system was applied in sampling the crawl acoustic signal of Tribolinm castaneum Herbst adults in granary. Preliminary experimentation indicated the rationality and practicability of the developed system and the proposed algorithm. They can implement the remote, real-time, and reliable wireless transmission for the acoustic signal sampled data of multiple points stored grain pests effectively.展开更多
Distributed Compressed Sensing (DCS) is an emerging field that exploits both intra- and inter-signal correlation structures and enables new distributed coding algorithms for multiple signal ensembles in wireless senso...Distributed Compressed Sensing (DCS) is an emerging field that exploits both intra- and inter-signal correlation structures and enables new distributed coding algorithms for multiple signal ensembles in wireless sensor networks. The DCS theory rests on the joint sparsity of a multi-signal ensemble. In this paper we propose a new mobile-agent-based Adaptive Data Fusion (ADF) algorithm to determine the minimum number of measurements each node required for perfectly joint reconstruction of multiple signal ensembles. We theoretically show that ADF provides the optimal strategy with as minimum total number of measurements as possible and hence reduces communication cost and network load. Simulation results indicate that ADF enjoys better performance than DCS and mobile-agent-based full data fusion algorithm including reconstruction performance and network energy efficiency.展开更多
A great challenge faced by wireless sensor networks(WSNs) is to reduce energy consumption of sensor nodes. Fortunately, the data gathering via random sensing can save energy of sensor nodes. Nevertheless, its randomne...A great challenge faced by wireless sensor networks(WSNs) is to reduce energy consumption of sensor nodes. Fortunately, the data gathering via random sensing can save energy of sensor nodes. Nevertheless, its randomness and density usually result in difficult implementations, high computation complexity and large storage spaces in practical settings. So the deterministic sparse sensing matrices are desired in some situations. However,it is difficult to guarantee the performance of deterministic sensing matrix by the acknowledged metrics. In this paper, we construct a class of deterministic sparse sensing matrices with statistical versions of restricted isometry property(St RIP) via regular low density parity check(RLDPC) matrices. The key idea of our construction is to achieve small mutual coherence of the matrices by confining the column weights of RLDPC matrices such that St RIP is satisfied. Besides, we prove that the constructed sensing matrices have the same scale of measurement numbers as the dense measurements. We also propose a data gathering method based on RLDPC matrix. Experimental results verify that the constructed sensing matrices have better reconstruction performance, compared to the Gaussian, Bernoulli, and CSLDPC matrices. And we also verify that the data gathering via RLDPC matrix can reduce energy consumption of WSNs.展开更多
An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Informa...An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.展开更多
For Wireless Sensor Networks （WSN） is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing...For Wireless Sensor Networks （WSN） is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing theory, study the wireless sensor network data conventional compression and network coding method. The linear network coding scheme based on sparse random projection theory of compressed sensing. Simulation results show that this system satisfies the requirements of the reconstruction error of packets needed to reduce the number of nodes to the total number of 30%, improves the efficiency of data communications in wireless sensor network, reduce the energy consumption of the system. With other wireless sensor network data compression algorithm, the proposed algorithm has the advantages of simple realization, the compression effect is good, especially suitable for resource limited, and the accuracy requirements are not particularly stringent in wireless sensor networks.展开更多
Target tracking is a challenging problem for wireless sensor networks because sensor nodes carry limited power recourses. Thus, scheduling of sensor nodes must focus on power conservation. It is possible to extend the...Target tracking is a challenging problem for wireless sensor networks because sensor nodes carry limited power recourses. Thus, scheduling of sensor nodes must focus on power conservation. It is possible to extend the lifetime of a network by dynamic clustering and duty cycling. Sensor Scheduling Algorithm Target Tracking-oriented is proposed in this paper. When the target occurs in the sensing filed, cluster and duty cycling algorithm is executed to scheduling sensor node to perform taking task. With the target moving, only one cluster is active, the other is in sleep state, which is efficient for conserving sensor nodes’ limited power. Using dynamic cluster and duty cycling technology can allocate efficiently sensor nodes’ limited energy and perform tasks coordinately.展开更多
In order to apply compressive sensing in wireless sensor network, inside the nodes cluster classified by the spatial correlation, we propose that a cluster head adopts free space optical communication with space divis...In order to apply compressive sensing in wireless sensor network, inside the nodes cluster classified by the spatial correlation, we propose that a cluster head adopts free space optical communication with space division multiple access, and a sensor node uses a modulating retro-reflector for communication. Thus while a random sampling matrix is used to guide the establishment of links between head cluster and sensor nodes, the random linear projection is accomplished. To establish multiple links at the same time, an optical space division multiple access antenna is designed. It works in fixed beams switching mode and consists of optic lens with a large field of view（FOV）, fiber array on the focal plane which is used to realize virtual channels segmentation, direction of arrival sensor, optical matrix switch and controller. Based on the angles of nodes＇ laser beams, by dynamically changing the route, optical matrix switch actualizes the multi-beam full duplex tracking receiving and transmission. Due to the structure of fiber array, there will be several fade zones both in the focal plane and in lens＇ FOV. In order to lower the impact of fade zones and harmonize multibeam, a fiber array adjustment is designed. By theoretical, simulated and experimental study, the antenna＇s qualitative feasibility is validated.展开更多
This study presents the architecture and design flow of smart mobile sensing systems that performs wireless sensor data transmission, data analysis and display in real-time. Multiple wireless protocols are used for se...This study presents the architecture and design flow of smart mobile sensing systems that performs wireless sensor data transmission, data analysis and display in real-time. Multiple wireless protocols are used for sensor data transmission including the Bluetooth, cellular data network and Wi-Fi for Internet access, and Near Field Communication (NFC). An Android smartphone is utilized to demonstrate the design concept of an Intelligent Personal Communication Node (iPCN) and to perform real-time sensor data acquisition, processing, analysis, display and transmission. Tested sensors include acceleration, temperature, electrocardiography (ECG) and phonocardiography (PCG). For computational capability tests, we have observed the signal processing performance of the smartphone by implementing fast Fourier transform (FFT) of the received ECG signal, and QRS detection algorithm for spontaneous heart beat rate (HBR) estimation. This system has also been tested for multiple sensor node communication and on-demand sensor data acquisition. The smart mobile sensing system can also be applied to any environment that requires real-time sensing and wireless remote monitoring.展开更多
Wireless Multimedia Sensor Networks(WMSN)are designed to transmit audio and video streams,still images,and scalar data.Multimedia transmission over wireless sensor networks has many applications,such as video surveill...Wireless Multimedia Sensor Networks(WMSN)are designed to transmit audio and video streams,still images,and scalar data.Multimedia transmission over wireless sensor networks has many applications,such as video surveillance,object tracking,telemedicine,theft control systems,and traffic monitoring.Researchers face many challenges,such as higher data rates,lower energy consumption,reliability,signal detection and estimation,uncertainty in network topology,quality of service(QoS),and security-and privacy-related issues to accomplish various applications of WMSN.This paper presents multiple input multiple output(MIMO)along with compressive sensing(CS)properties to improve system performance in terms of energy consumption and QoS in deep fade environments.The CS theory model has been proposed to reduce energy consumption by taking fewer measurements of the original signal or information and reconstructing it with acceptable image quality at the receiver side.The transmission and processing energy can be reduced by transmitting fewer measurements from the sensor side itself.The MIMO model and CS algorithm have been simulated,and results show that CS performs well on images.展开更多
WSNs （wireless sensor networks） can be used for railway infrastructure inspection and vehicle health monitoring. SHM （structural health monitoring） systems have a great potential to improve regular operation, secu...WSNs （wireless sensor networks） can be used for railway infrastructure inspection and vehicle health monitoring. SHM （structural health monitoring） systems have a great potential to improve regular operation, security and maintenance routine of structures with estimating the state of its health and detecting the changes that affect its performance. This is vital for the development, upgrading, and expansion of railway networks. The work presented in this paper aims at the possible use of acoustic sensors coupled with ZigBee modules for health monitoring of rails. The detection principle is based on acoustic noise correlation techniques. Experiments have been performed in a rail sample to confirm the validity of acoustic noise correlation techniques in the rail. A wireless communication platform prototype based on the ZigBee/IEEE 802.15.4 technology has been implemented and deployed on a rail sample. Once the signals from the structure are collected, sensor data are transmitted through a ZigBee solution to the processing unit.展开更多
A wireless acoustic sensor network was realized using wireless sensor nodes equipped with microphone condensers,in which its sensor nodes were configured to capture poultry sound data and transmit it via the network t...A wireless acoustic sensor network was realized using wireless sensor nodes equipped with microphone condensers,in which its sensor nodes were configured to capture poultry sound data and transmit it via the network to a collection point.A high performance computer can process these large volumes of animal audio signals under different behaviors.By performing data signal processing and analyzing the audio signal,poultry sound can be achieved and then transformed into their corresponding behavioral modes for welfare assessment.In this study,compressive sensing algorithm was developed in consideration of the balance between the power saving from compression ratio and the computational cost,and a low power consumption as well as an inexpensive sensor node was designed as the elementary unit of poultry acoustic data collecting and transmission.Then,a Zigbee-based wireless acoustic sensor network was developed to meet the challenges of short transmission range and limited resources of storage and energy.Experimental results demonstrate that the compressive sensing algorithm can improve the communication performances of the wireless acoustic sensor network with high reliability,low packet loss rate and low energy consumption.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52201403,52201401,52071200,52102397,61701299,51709167)the National Key Research and Development Program(No.2021YFC2801002)+4 种基金the China Postdoctoral Science Foundation(Nos.2021M 700790,2022M712027)the Fund of National Engineering Research Center for Water Transport Safety(No.A2022003)the Foundation for Jiangsu Key Laboratory of Traffic and Transportation Security(No.TTS2021-05)the Fund of Hubei Key Laboratory of Inland Shipping Technology(No.NHHY2021002)the Top-Notch Innovative Program for Postgraduates of Shanghai Maritime University(Nos.2019YBR006,2019YBR002).
文摘In marine wireless sensor networks(MWSNs),an appropriate routing protocol is the key to the collaborative collection and efficient transmission of massive data.However,designing an appropriate routing protocol under the condition of sparse marine node deployment,highly dynamic network topology,and limited node energy is complicated.Moreover,the absence of continuous endto-end connection introduces further difficulties in the design of routing protocols.In this case,we present a novel energy-efficient opportunistic routing(Novel Energy-Efficient Opportunistic Routing,NEOR)protocol for MWSNs that is based on compressed sensing and power control.First,a lightweight time-series prediction method-weighted moving average method is proposed to predict the packet advancement value such that the number of location information that is exchanged among a node and its neighbor nodes can be minimized.Second,an adaptive power control mechanism is presented to determine the optimal transmitting power and candidate nodeset on the basis of node mobility,packet advancement,communication link quality,and remaining node energy.Subsequently,a timer-based scheduling algorithm is utilized to coordinate packet forwarding to avoid packet conflict.Furthermore,we introduce the compressed sensing theory to compress perceptual data at source nodes and reconstruct the original data at sink nodes.Therefore,energy consumption in the MWSNs is greatly reduced due to the decrease in the amount of data perception and transmission.Numerical simulation experiments are carried out in a wide range of marine scenarios to verify the superiority of our approach over selected benchmark algorithms.
文摘An energy-saving algorithm for wireless sensor networks based on network coding and compressed sensing(CS-NCES)is proposed in this paper.Along with considering the correlations of data spatial and temporal,the algorithm utilizes the similarities between the encoding matrix of network coding and the measurement matrix of compressed sensing.The source node firstly encodes the data,then compresses the coding data by cot-npressed sensing over finite fields.Compared with the network coding scheme,simulation results show that CS-NCES reduces the energy consumption about 25.30/0-34.50/0 and improves the efficiency of data reconstruction about 1.56%-5.98%.The proposed algorithm can not only enhance the usability of network coding in wireless sensor networks,but also improve the network performance.
文摘In order to achieve the acoustic signal distributed acquisition of stored grain pests, a novel acoustic signal acquisition system was presented based on the wireless sensor networks. And the system architecture, hardware configuration, and software were introduced in detail. Considering bandwidth limitation of wireless sensor networks, random sampling algorithm based on the compressed sensing theory was proposed. The developed acoustic signal acquisition system was applied in sampling the crawl acoustic signal of Tribolinm castaneum Herbst adults in granary. Preliminary experimentation indicated the rationality and practicability of the developed system and the proposed algorithm. They can implement the remote, real-time, and reliable wireless transmission for the acoustic signal sampled data of multiple points stored grain pests effectively.
文摘Distributed Compressed Sensing (DCS) is an emerging field that exploits both intra- and inter-signal correlation structures and enables new distributed coding algorithms for multiple signal ensembles in wireless sensor networks. The DCS theory rests on the joint sparsity of a multi-signal ensemble. In this paper we propose a new mobile-agent-based Adaptive Data Fusion (ADF) algorithm to determine the minimum number of measurements each node required for perfectly joint reconstruction of multiple signal ensembles. We theoretically show that ADF provides the optimal strategy with as minimum total number of measurements as possible and hence reduces communication cost and network load. Simulation results indicate that ADF enjoys better performance than DCS and mobile-agent-based full data fusion algorithm including reconstruction performance and network energy efficiency.
基金supported by the National Natural Science Foundation of China(61307121)ABRP of Datong(2017127)the Ph.D.’s Initiated Research Projects of Datong University(2013-B-17,2015-B-05)
文摘A great challenge faced by wireless sensor networks(WSNs) is to reduce energy consumption of sensor nodes. Fortunately, the data gathering via random sensing can save energy of sensor nodes. Nevertheless, its randomness and density usually result in difficult implementations, high computation complexity and large storage spaces in practical settings. So the deterministic sparse sensing matrices are desired in some situations. However,it is difficult to guarantee the performance of deterministic sensing matrix by the acknowledged metrics. In this paper, we construct a class of deterministic sparse sensing matrices with statistical versions of restricted isometry property(St RIP) via regular low density parity check(RLDPC) matrices. The key idea of our construction is to achieve small mutual coherence of the matrices by confining the column weights of RLDPC matrices such that St RIP is satisfied. Besides, we prove that the constructed sensing matrices have the same scale of measurement numbers as the dense measurements. We also propose a data gathering method based on RLDPC matrix. Experimental results verify that the constructed sensing matrices have better reconstruction performance, compared to the Gaussian, Bernoulli, and CSLDPC matrices. And we also verify that the data gathering via RLDPC matrix can reduce energy consumption of WSNs.
基金Supported by the National Natural Science Foundation of China (No. 61102066, 60972058)the China Postdoctoral Science Foundation (No. 2012M511365)the Scientific Research Project of Zhejiang Provincial Education Department (No. Y201119890)
文摘An Adaptive Measurement Scheme (AMS) is investigated with Compressed Sensing (CS) theory in Cognitive Wireless Sensor Network (C-WSN). Local sensing information is collected via energy detection with Analog-to-Information Converter (AIC) at massive cognitive sensors, and sparse representation is considered with the exploration of spatial temporal correlation structure of detected signals. Adaptive measurement matrix is designed in AMS, which is based on maximum energy subset selection. Energy subset is calculated with sparse transformation of sensing information, and maximum energy subset is selected as the row vector of adaptive measurement matrix. In addition, the measurement matrix is constructed by orthogonalization of those selected row vectors, which also satisfies the Restricted Isometry Property (RIP) in CS theory. Orthogonal Matching Pursuit (OMP) reconstruction algorithm is implemented at sink node to recover original information. Simulation results are performed with the comparison of Random Measurement Scheme (RMS). It is revealed that, signal reconstruction effect based on AMS is superior to conventional RMS Gaussian measurement. Moreover, AMS has better detection performance than RMS at lower compression rate region, and it is suitable for large-scale C-WSN wideband spectrum sensing.
文摘For Wireless Sensor Networks （WSN） is responsible for sensing, collecting, processing and monitoring of environmental data, but it might be limited in resources. This paper describes in detail the compressed sensing theory, study the wireless sensor network data conventional compression and network coding method. The linear network coding scheme based on sparse random projection theory of compressed sensing. Simulation results show that this system satisfies the requirements of the reconstruction error of packets needed to reduce the number of nodes to the total number of 30%, improves the efficiency of data communications in wireless sensor network, reduce the energy consumption of the system. With other wireless sensor network data compression algorithm, the proposed algorithm has the advantages of simple realization, the compression effect is good, especially suitable for resource limited, and the accuracy requirements are not particularly stringent in wireless sensor networks.
文摘Target tracking is a challenging problem for wireless sensor networks because sensor nodes carry limited power recourses. Thus, scheduling of sensor nodes must focus on power conservation. It is possible to extend the lifetime of a network by dynamic clustering and duty cycling. Sensor Scheduling Algorithm Target Tracking-oriented is proposed in this paper. When the target occurs in the sensing filed, cluster and duty cycling algorithm is executed to scheduling sensor node to perform taking task. With the target moving, only one cluster is active, the other is in sleep state, which is efficient for conserving sensor nodes’ limited power. Using dynamic cluster and duty cycling technology can allocate efficiently sensor nodes’ limited energy and perform tasks coordinately.
基金supported by the National Natural Science Foundation of China(61372069)and the"111"Project(B08038)
文摘In order to apply compressive sensing in wireless sensor network, inside the nodes cluster classified by the spatial correlation, we propose that a cluster head adopts free space optical communication with space division multiple access, and a sensor node uses a modulating retro-reflector for communication. Thus while a random sampling matrix is used to guide the establishment of links between head cluster and sensor nodes, the random linear projection is accomplished. To establish multiple links at the same time, an optical space division multiple access antenna is designed. It works in fixed beams switching mode and consists of optic lens with a large field of view（FOV）, fiber array on the focal plane which is used to realize virtual channels segmentation, direction of arrival sensor, optical matrix switch and controller. Based on the angles of nodes＇ laser beams, by dynamically changing the route, optical matrix switch actualizes the multi-beam full duplex tracking receiving and transmission. Due to the structure of fiber array, there will be several fade zones both in the focal plane and in lens＇ FOV. In order to lower the impact of fade zones and harmonize multibeam, a fiber array adjustment is designed. By theoretical, simulated and experimental study, the antenna＇s qualitative feasibility is validated.
文摘This study presents the architecture and design flow of smart mobile sensing systems that performs wireless sensor data transmission, data analysis and display in real-time. Multiple wireless protocols are used for sensor data transmission including the Bluetooth, cellular data network and Wi-Fi for Internet access, and Near Field Communication (NFC). An Android smartphone is utilized to demonstrate the design concept of an Intelligent Personal Communication Node (iPCN) and to perform real-time sensor data acquisition, processing, analysis, display and transmission. Tested sensors include acceleration, temperature, electrocardiography (ECG) and phonocardiography (PCG). For computational capability tests, we have observed the signal processing performance of the smartphone by implementing fast Fourier transform (FFT) of the received ECG signal, and QRS detection algorithm for spontaneous heart beat rate (HBR) estimation. This system has also been tested for multiple sensor node communication and on-demand sensor data acquisition. The smart mobile sensing system can also be applied to any environment that requires real-time sensing and wireless remote monitoring.
文摘Wireless Multimedia Sensor Networks(WMSN)are designed to transmit audio and video streams,still images,and scalar data.Multimedia transmission over wireless sensor networks has many applications,such as video surveillance,object tracking,telemedicine,theft control systems,and traffic monitoring.Researchers face many challenges,such as higher data rates,lower energy consumption,reliability,signal detection and estimation,uncertainty in network topology,quality of service(QoS),and security-and privacy-related issues to accomplish various applications of WMSN.This paper presents multiple input multiple output(MIMO)along with compressive sensing(CS)properties to improve system performance in terms of energy consumption and QoS in deep fade environments.The CS theory model has been proposed to reduce energy consumption by taking fewer measurements of the original signal or information and reconstructing it with acceptable image quality at the receiver side.The transmission and processing energy can be reduced by transmitting fewer measurements from the sensor side itself.The MIMO model and CS algorithm have been simulated,and results show that CS performs well on images.
文摘WSNs （wireless sensor networks） can be used for railway infrastructure inspection and vehicle health monitoring. SHM （structural health monitoring） systems have a great potential to improve regular operation, security and maintenance routine of structures with estimating the state of its health and detecting the changes that affect its performance. This is vital for the development, upgrading, and expansion of railway networks. The work presented in this paper aims at the possible use of acoustic sensors coupled with ZigBee modules for health monitoring of rails. The detection principle is based on acoustic noise correlation techniques. Experiments have been performed in a rail sample to confirm the validity of acoustic noise correlation techniques in the rail. A wireless communication platform prototype based on the ZigBee/IEEE 802.15.4 technology has been implemented and deployed on a rail sample. Once the signals from the structure are collected, sensor data are transmitted through a ZigBee solution to the processing unit.
基金the General Program of National Natural Science Foundation of China(No.11364029,No.61461042)Key Projects of National Science and Technology Ministry of China(No.2014BAD08B05)“Prairie talent”Industrial Innovation Team project of Inner Mongolia(No.2014-27).
文摘A wireless acoustic sensor network was realized using wireless sensor nodes equipped with microphone condensers,in which its sensor nodes were configured to capture poultry sound data and transmit it via the network to a collection point.A high performance computer can process these large volumes of animal audio signals under different behaviors.By performing data signal processing and analyzing the audio signal,poultry sound can be achieved and then transformed into their corresponding behavioral modes for welfare assessment.In this study,compressive sensing algorithm was developed in consideration of the balance between the power saving from compression ratio and the computational cost,and a low power consumption as well as an inexpensive sensor node was designed as the elementary unit of poultry acoustic data collecting and transmission.Then,a Zigbee-based wireless acoustic sensor network was developed to meet the challenges of short transmission range and limited resources of storage and energy.Experimental results demonstrate that the compressive sensing algorithm can improve the communication performances of the wireless acoustic sensor network with high reliability,low packet loss rate and low energy consumption.
