With the development of the Internet of Things(IoT),diverse wireless devices are increasing rapidly.Those devices have different wireless interfaces that generate incompatible wireless signals.Each signal has its own ...With the development of the Internet of Things(IoT),diverse wireless devices are increasing rapidly.Those devices have different wireless interfaces that generate incompatible wireless signals.Each signal has its own physical characteristics with signal modulation and demodulation scheme.When there exist different wireless devices,they can suffer from severe Cross-Technology Interferences(CTI).To reduce the communication overhead due to the CTI in the real IoT environment,a central coordinator can be able to detect and identify wireless signals existing in the same communication areas.This paper investigates how to classify various radio signals using Convolutional Neural Networks(CNN),Long Short-TermMemory(LSTM)and attention mechanism.CNN can reduce the amount of computation by reducing weights by using convolution,and LSTM belonging to RNNmodels can alleviate the long-term dependence problem.Furthermore,attention mechanism can reduce the short-term memory problem of RNNs by reexamining the data output from the decoder and the entire data entered into the encoder at every point in time.To accurately classify radio signals according to their weights,we design a model based on CNN,LSTM,and attention mechanism.As a result,we propose a model CLARINet that can classify original data by minimizing the loss and detects changes in sequences.In a case of the real IoT environment with Wi-Fi,Bluetooth and ZigBee devices,we can normally obtain wireless signals from 10 to 20 dB.The accuracy of CLARINet’s radio signal classification with CNN-LSTM and attention mechanism can be seen that signal-to-noise ratio(SNR)exhibits high accuracy at 16 dB to about 92.03%.展开更多
The wireless communication system's performance is greatly constrained by the wireless channel characteristics,especially in some specific environment.Therefore,signal transmission will be greatly impacted even if...The wireless communication system's performance is greatly constrained by the wireless channel characteristics,especially in some specific environment.Therefore,signal transmission will be greatly impacted even if not in a complicated topography.Testing results show that it is hardly to characterize the radio propagation properties for the antenna installed on the ground.In order to ensure a successful communication,the radio frequency(RF)wireless signal intensity monitor system was designed.We can get the wireless link transmission loss through measuring signal strength from received node.The test shows that the near-ground wireless signal propagation characteristics still can be characterized by the log distance propagation loss model.These results will conduce to studying the transmission characteristic of Near-Earth wireless signals and will predict the coverage of the earth's surface wireless sensor network.展开更多
Open-set recognition(OSR)is a realistic problem in wireless signal recogni-tion,which means that during the inference phase there may appear unknown classes not seen in the training phase.The method of intra-class spl...Open-set recognition(OSR)is a realistic problem in wireless signal recogni-tion,which means that during the inference phase there may appear unknown classes not seen in the training phase.The method of intra-class splitting(ICS)that splits samples of known classes to imitate unknown classes has achieved great performance.However,this approach relies too much on the predefined splitting ratio and may face huge performance degradation in new environment.In this paper,we train a multi-task learning(MTL)net-work based on the characteristics of wireless signals to improve the performance in new scenes.Besides,we provide a dynamic method to decide the splitting ratio per class to get more precise outer samples.To be specific,we make perturbations to the sample from the center of one class toward its adversarial direction and the change point of confidence scores during this process is used as the splitting threshold.We conduct several experi-ments on one wireless signal dataset collected at 2.4 GHz ISM band by LimeSDR and one open modulation recognition dataset,and the analytical results demonstrate the effective-ness of the proposed method.展开更多
针对雷达、微波辐射计及无线通信系统中大阵元最小冗余线性阵列(minimum redundancy linear array,MRLA)的获取难题——计算机长时间运算仅得有限解、易遗漏适配应用的最优阵列,通过分析L上元素最少的受限差基、最大连续基线长为L的MRL...针对雷达、微波辐射计及无线通信系统中大阵元最小冗余线性阵列(minimum redundancy linear array,MRLA)的获取难题——计算机长时间运算仅得有限解、易遗漏适配应用的最优阵列,通过分析L上元素最少的受限差基、最大连续基线长为L的MRLA、长度是L的完美稀疏尺的刻度值、L条边的极小优美图顶点标号各自定义的条件,经循环论证,证明四者数学等价;证明得到:线性阵列成对存在;线性阵列的冗余度≥1,阵元数超过4时冗余度>1;若MRLA最大连续基线长度为L且阵元数为n,则最大连续基线长度为L+1的MRLA阵元数不超过n+1.基于大规模MRLA数据分析,提出假设:冗余度≤1.5的线性阵列可视为MRLA.研究还发现了两类新型线性阵列解析公式,能高效筛选出无穷多的MRLA配置模式(即均为完美稀疏尺的刻度数值),并可根据实际需求灵活设定线性阵列冗余度的筛选阈值,为MRLA的应用和完美稀疏尺的设计提供了理论支撑.展开更多
In the wireless localization application, multipath propagation seriously affects the localization accuracy. This paper presents two algorithms to solve the multipath problem. Firstly, we improve the Line of Possible ...In the wireless localization application, multipath propagation seriously affects the localization accuracy. This paper presents two algorithms to solve the multipath problem. Firstly, we improve the Line of Possible Mobile Device(LPMD) algorithm by optimizing the utilization of the direct paths for single-bound scattering scenario. Secondly, the signal path reckoning method with the assistance of geographic information system is proposed to solve the problem of localization with multi-bound scattering paths. With the building model's idealization, the proposed method refers to the idea of ray tracing and dead reckoning. According to the rule of wireless signal reflection, the signal propagation path is reckoned using the measurements of emission angle and propagation distance, and then the estimated location can be obtained. Simulation shows that the proposed method obtains better results than the existing geometric localization methods in multipath environment when the angle error is controlled.展开更多
Wireless sensor networks （WSNs） are based on monitoring or managing the sensing area by using the location information with sensor nodes. Most sensor nodes require hardware support or receive packets with location i...Wireless sensor networks （WSNs） are based on monitoring or managing the sensing area by using the location information with sensor nodes. Most sensor nodes require hardware support or receive packets with location information to estimate their locations, which needs lots of time or costs. In this paper we proposed a localization mechanism using a mobile reference node （MRN） and trilateration in WSNs to reduce the energy consumption and location error. The simulation results demonstrate that the proposed mechanism can obtain more unknown nodes locations by the mobile reference node moving scheme and will decreases the energy consumption and average ocation error.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
Human body posture recognition has attracted considerable attention in recent years in wireless body area networks(WBAN). In order to precisely recognize human body posture,many recognition algorithms have been propos...Human body posture recognition has attracted considerable attention in recent years in wireless body area networks(WBAN). In order to precisely recognize human body posture,many recognition algorithms have been proposed.However, the recognition rate is relatively low. In this paper, we apply back propagation(BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude(SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications.展开更多
Wireless Sensor network (WSN) is an emerging technology and has great potential to be employed in critical situations. The development of wireless sensor networks was originally motivated by military applications like...Wireless Sensor network (WSN) is an emerging technology and has great potential to be employed in critical situations. The development of wireless sensor networks was originally motivated by military applications like battlefield surveillance. However, Wireless Sensor Networks are also used in many areas such as Industrial, Civilian, Health, Habitat Monitoring, Environmental, Military, Home and Office application areas. Detection and tracking of targets (eg. animal, vehicle) as it moves through a sensor network has become an increasingly important application for sensor networks. The key advantage of WSN is that the network can be deployed on the fly and can operate unattended, without the need for any pre-existing infrastructure and with little maintenance. The system will estimate and track the target based on the spatial differences of the target signal strength detected by the sensors at different locations. Magnetic and acoustic sensors and the signals captured by these sensors are of present interest in the study. The system is made up of three components for detecting and tracking the moving objects. The first component consists of inexpensive off-the shelf wireless sensor devices, such as MicaZ motes, capable of measuring acoustic and magnetic signals generated by vehicles. The second component is responsible for the data aggregation. The third component of the system is responsible for data fusion algorithms. This paper inspects the sensors available in the market and its strengths and weakness and also some of the vehicle detection and tracking algorithms and their classification. This work focuses the overview of each algorithm for detection and tracking and compares them based on evaluation parameters.展开更多
A single sensor is used to obtain welding information in welding monitoring process, but this method has some shortcomings. In order to obtain more comprehensive and reliable welding information, this paper designed a...A single sensor is used to obtain welding information in welding monitoring process, but this method has some shortcomings. In order to obtain more comprehensive and reliable welding information, this paper designed and built a welding multi-information wireless monitoring system with STM32-F407ZET6 as the control core and ALK8266 as the wireless transmission module. Real-time acquisition, transmission and display of electric arc signal and welding image information are realized in the monitoring system. This paper mainly introduces the hardware and software core of the monitoring system. At the same time, the signal collected by the monitoring system is compared with the original signal, and the accuracy of the remote monitoring system is tested. The monitoring system is used in welding test. The test results show that the accuracy of the monitoring system meets the requirements, and the on-line monitoring of electric arc signal and welding image can be realized in the welding process.展开更多
A wireless body area network （WBAN） enables real-time monitoring of physiological signals and helps with the early detection of life-threatening diseases. WBAN nodes can be located on, inside, or in close proximity ...A wireless body area network （WBAN） enables real-time monitoring of physiological signals and helps with the early detection of life-threatening diseases. WBAN nodes can be located on, inside, or in close proximity to the body in order to detect vital signals. Measurements from sensors are processed and transmitted over wireless channels. Issues in sensing, signal processing, and com-munication have to be addressed before WBAN can be implemented. In this paper, we survey recent advances in research on sig-nal processing for the sensor measurements, and we describe aspects of communication based on IEEE 802.15.6. We also discuss state-of-the-art WBAN channel modeling in all the frequencies specified by IEEE 802.15.6 as well as the need for new channel models for new different frequencies.展开更多
Radio coverage directly affects the network connectivity, which is the foundational issue to ensure the normal operation of the network. Many efforts have been made to estimate the radio coverage of sensor nodes. The ...Radio coverage directly affects the network connectivity, which is the foundational issue to ensure the normal operation of the network. Many efforts have been made to estimate the radio coverage of sensor nodes. The existing approaches (often RSSI measurement-based), however, suffer from heavy measurement cost and are not well suitable for the large-scale densely deployed WSNs. NRC-Map, a novel algorithm is put forward for sensor nodes radio coverage mapping. The algorithm is based on the RSSI values collected by the neighbor nodes. According to the spatial relationship, neighbor nodes are mapping to several overlapped sectors. By use of the least squares fitting method, a log-distance path loss model is established for each sector. Then, the max radius of each sector is computed according to the path loss model and the given signal attenuation threshold. Finally, all the sectors are overlapped to estimate the node radio coverage. Experimental results show that the method is simple and effectively improve the prediction accuracy of the sensor node radio coverage.展开更多
基金This work was supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(MSIT)(No.2021R1F1A1063319)。
文摘With the development of the Internet of Things(IoT),diverse wireless devices are increasing rapidly.Those devices have different wireless interfaces that generate incompatible wireless signals.Each signal has its own physical characteristics with signal modulation and demodulation scheme.When there exist different wireless devices,they can suffer from severe Cross-Technology Interferences(CTI).To reduce the communication overhead due to the CTI in the real IoT environment,a central coordinator can be able to detect and identify wireless signals existing in the same communication areas.This paper investigates how to classify various radio signals using Convolutional Neural Networks(CNN),Long Short-TermMemory(LSTM)and attention mechanism.CNN can reduce the amount of computation by reducing weights by using convolution,and LSTM belonging to RNNmodels can alleviate the long-term dependence problem.Furthermore,attention mechanism can reduce the short-term memory problem of RNNs by reexamining the data output from the decoder and the entire data entered into the encoder at every point in time.To accurately classify radio signals according to their weights,we design a model based on CNN,LSTM,and attention mechanism.As a result,we propose a model CLARINet that can classify original data by minimizing the loss and detects changes in sequences.In a case of the real IoT environment with Wi-Fi,Bluetooth and ZigBee devices,we can normally obtain wireless signals from 10 to 20 dB.The accuracy of CLARINet’s radio signal classification with CNN-LSTM and attention mechanism can be seen that signal-to-noise ratio(SNR)exhibits high accuracy at 16 dB to about 92.03%.
文摘The wireless communication system's performance is greatly constrained by the wireless channel characteristics,especially in some specific environment.Therefore,signal transmission will be greatly impacted even if not in a complicated topography.Testing results show that it is hardly to characterize the radio propagation properties for the antenna installed on the ground.In order to ensure a successful communication,the radio frequency(RF)wireless signal intensity monitor system was designed.We can get the wireless link transmission loss through measuring signal strength from received node.The test shows that the near-ground wireless signal propagation characteristics still can be characterized by the log distance propagation loss model.These results will conduce to studying the transmission characteristic of Near-Earth wireless signals and will predict the coverage of the earth's surface wireless sensor network.
文摘Open-set recognition(OSR)is a realistic problem in wireless signal recogni-tion,which means that during the inference phase there may appear unknown classes not seen in the training phase.The method of intra-class splitting(ICS)that splits samples of known classes to imitate unknown classes has achieved great performance.However,this approach relies too much on the predefined splitting ratio and may face huge performance degradation in new environment.In this paper,we train a multi-task learning(MTL)net-work based on the characteristics of wireless signals to improve the performance in new scenes.Besides,we provide a dynamic method to decide the splitting ratio per class to get more precise outer samples.To be specific,we make perturbations to the sample from the center of one class toward its adversarial direction and the change point of confidence scores during this process is used as the splitting threshold.We conduct several experi-ments on one wireless signal dataset collected at 2.4 GHz ISM band by LimeSDR and one open modulation recognition dataset,and the analytical results demonstrate the effective-ness of the proposed method.
文摘针对雷达、微波辐射计及无线通信系统中大阵元最小冗余线性阵列(minimum redundancy linear array,MRLA)的获取难题——计算机长时间运算仅得有限解、易遗漏适配应用的最优阵列,通过分析L上元素最少的受限差基、最大连续基线长为L的MRLA、长度是L的完美稀疏尺的刻度值、L条边的极小优美图顶点标号各自定义的条件,经循环论证,证明四者数学等价;证明得到:线性阵列成对存在;线性阵列的冗余度≥1,阵元数超过4时冗余度>1;若MRLA最大连续基线长度为L且阵元数为n,则最大连续基线长度为L+1的MRLA阵元数不超过n+1.基于大规模MRLA数据分析,提出假设:冗余度≤1.5的线性阵列可视为MRLA.研究还发现了两类新型线性阵列解析公式,能高效筛选出无穷多的MRLA配置模式(即均为完美稀疏尺的刻度数值),并可根据实际需求灵活设定线性阵列冗余度的筛选阈值,为MRLA的应用和完美稀疏尺的设计提供了理论支撑.
基金supported by the National Natural Science Foundation of China (61471031)the Fundamental Research Funds for the Central Universities,Beijing Jiaotong University (2013JBZ001)+2 种基金National Science and Technology Major Project (2016ZX03001014006)the Open Research Fund of National Mobile Communications Research Laboratory,Southeast University (No.2017D14)Shenzhen Peacock Program under Grant No.KQJSCX20160226193545
文摘In the wireless localization application, multipath propagation seriously affects the localization accuracy. This paper presents two algorithms to solve the multipath problem. Firstly, we improve the Line of Possible Mobile Device(LPMD) algorithm by optimizing the utilization of the direct paths for single-bound scattering scenario. Secondly, the signal path reckoning method with the assistance of geographic information system is proposed to solve the problem of localization with multi-bound scattering paths. With the building model's idealization, the proposed method refers to the idea of ray tracing and dead reckoning. According to the rule of wireless signal reflection, the signal propagation path is reckoned using the measurements of emission angle and propagation distance, and then the estimated location can be obtained. Simulation shows that the proposed method obtains better results than the existing geometric localization methods in multipath environment when the angle error is controlled.
文摘Wireless sensor networks （WSNs） are based on monitoring or managing the sensing area by using the location information with sensor nodes. Most sensor nodes require hardware support or receive packets with location information to estimate their locations, which needs lots of time or costs. In this paper we proposed a localization mechanism using a mobile reference node （MRN） and trilateration in WSNs to reduce the energy consumption and location error. The simulation results demonstrate that the proposed mechanism can obtain more unknown nodes locations by the mobile reference node moving scheme and will decreases the energy consumption and average ocation error.
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.61074165 and No.61273064)Jilin Provincial Science&Technology Department Key Scientific and Technological Project(No.20140204034GX)Jilin Province Development and Reform Commission Project(No.2015Y043)
文摘Human body posture recognition has attracted considerable attention in recent years in wireless body area networks(WBAN). In order to precisely recognize human body posture,many recognition algorithms have been proposed.However, the recognition rate is relatively low. In this paper, we apply back propagation(BP) neural network as a classifier to recognizing human body posture, where signals are collected from VG350 acceleration sensor and a posture signal collection system based on WBAN is designed. Human body signal vector magnitude(SVM) and tri-axial acceleration sensor data are used to describe the human body postures. We are able to recognize 4postures: Walk, Run, Squat and Sit. Our posture recognition rate is up to 91.67%. Furthermore, we find an implied relationship between hidden layer neurons and the posture recognition rate. The proposed human body posture recognition algorithm lays the foundation for the subsequent applications.
文摘Wireless Sensor network (WSN) is an emerging technology and has great potential to be employed in critical situations. The development of wireless sensor networks was originally motivated by military applications like battlefield surveillance. However, Wireless Sensor Networks are also used in many areas such as Industrial, Civilian, Health, Habitat Monitoring, Environmental, Military, Home and Office application areas. Detection and tracking of targets (eg. animal, vehicle) as it moves through a sensor network has become an increasingly important application for sensor networks. The key advantage of WSN is that the network can be deployed on the fly and can operate unattended, without the need for any pre-existing infrastructure and with little maintenance. The system will estimate and track the target based on the spatial differences of the target signal strength detected by the sensors at different locations. Magnetic and acoustic sensors and the signals captured by these sensors are of present interest in the study. The system is made up of three components for detecting and tracking the moving objects. The first component consists of inexpensive off-the shelf wireless sensor devices, such as MicaZ motes, capable of measuring acoustic and magnetic signals generated by vehicles. The second component is responsible for the data aggregation. The third component of the system is responsible for data fusion algorithms. This paper inspects the sensors available in the market and its strengths and weakness and also some of the vehicle detection and tracking algorithms and their classification. This work focuses the overview of each algorithm for detection and tracking and compares them based on evaluation parameters.
文摘A single sensor is used to obtain welding information in welding monitoring process, but this method has some shortcomings. In order to obtain more comprehensive and reliable welding information, this paper designed and built a welding multi-information wireless monitoring system with STM32-F407ZET6 as the control core and ALK8266 as the wireless transmission module. Real-time acquisition, transmission and display of electric arc signal and welding image information are realized in the monitoring system. This paper mainly introduces the hardware and software core of the monitoring system. At the same time, the signal collected by the monitoring system is compared with the original signal, and the accuracy of the remote monitoring system is tested. The monitoring system is used in welding test. The test results show that the accuracy of the monitoring system meets the requirements, and the on-line monitoring of electric arc signal and welding image can be realized in the welding process.
基金performed,in part,of the research project Medicalsensing,localization and communications using ultra widebandtechnology(MELODY)contract no.285885,and Adaptive Security forSmart Internet of Things in eHealth(ASSET)contract no.213131,whichboth are funded by the Research Council of Norway
文摘A wireless body area network （WBAN） enables real-time monitoring of physiological signals and helps with the early detection of life-threatening diseases. WBAN nodes can be located on, inside, or in close proximity to the body in order to detect vital signals. Measurements from sensors are processed and transmitted over wireless channels. Issues in sensing, signal processing, and com-munication have to be addressed before WBAN can be implemented. In this paper, we survey recent advances in research on sig-nal processing for the sensor measurements, and we describe aspects of communication based on IEEE 802.15.6. We also discuss state-of-the-art WBAN channel modeling in all the frequencies specified by IEEE 802.15.6 as well as the need for new channel models for new different frequencies.
文摘Radio coverage directly affects the network connectivity, which is the foundational issue to ensure the normal operation of the network. Many efforts have been made to estimate the radio coverage of sensor nodes. The existing approaches (often RSSI measurement-based), however, suffer from heavy measurement cost and are not well suitable for the large-scale densely deployed WSNs. NRC-Map, a novel algorithm is put forward for sensor nodes radio coverage mapping. The algorithm is based on the RSSI values collected by the neighbor nodes. According to the spatial relationship, neighbor nodes are mapping to several overlapped sectors. By use of the least squares fitting method, a log-distance path loss model is established for each sector. Then, the max radius of each sector is computed according to the path loss model and the given signal attenuation threshold. Finally, all the sectors are overlapped to estimate the node radio coverage. Experimental results show that the method is simple and effectively improve the prediction accuracy of the sensor node radio coverage.
