This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed alg...This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.展开更多
As a kind of brand-new technology, radio frequency identification management, data control and acquisition. This paper introduced food safety system construction, analyzed the advantages and problems in dairy modem su...As a kind of brand-new technology, radio frequency identification management, data control and acquisition. This paper introduced food safety system construction, analyzed the advantages and problems in dairy modem suggestions for solution according to the practical situation. (RFID) plays an important role in dairy information tracing and culture function extension of managing breeding technology, and finally put forward some展开更多
Radio frequency identification(RFID) is a new type of non-contact automatic identification technology.Due to its low energy consumption,low cost,and its adaptability to harsh environments,it has been applied to many f...Radio frequency identification(RFID) is a new type of non-contact automatic identification technology.Due to its low energy consumption,low cost,and its adaptability to harsh environments,it has been applied to many fields.In the RFID systems,data collision is inevitable when the reader sends a communication request and multiple tags respond with simultaneous data transmission.Data collision is prone to causing problems such as:identification delay,spectrum resource waste,a decreased system throughput rate,etc.Therefore,an efficient,stable anti-collision protocol is crucial for RFID systems.This research analysed the current research into RFID anticollision protocols and summarised means for its improvement through the mechanism of implementation of different types anticollision protocols.Finally,a new direction is proposed for the future development of RFID anti-collision protocol systems.展开更多
In order to construct a resource-saving and environmentfriendly society,the advantages of radio frequency identification(RFID) were considered.And it put forward the idea of introducing RFID in the recycling activitie...In order to construct a resource-saving and environmentfriendly society,the advantages of radio frequency identification(RFID) were considered.And it put forward the idea of introducing RFID in the recycling activities of waste products.Taking into account such elements as the technical level of RFID,cost saving from remanufacturing and the cost of RFID tags,both centralized and decentralized supply chain models with different participants in waste product collection were created,in order to determine the optimal pricing strategy and RFID technical level.In the end,sensitivity analyses were conducted to analyze the impacts of scaling parameter for additional increased recovery rate with RFID on pricing and RFID technology level,and impacts of cost saving on the profits of participants in different remanufacturing closed-loop supply chain models.展开更多
Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emi...Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.展开更多
In a passive ultra-high frequency(UHF)radio frequency identification(RFID)system,the recovery of collided tag signals on a physical layer can enhance identification efficiency.However,frequency drift is very common in...In a passive ultra-high frequency(UHF)radio frequency identification(RFID)system,the recovery of collided tag signals on a physical layer can enhance identification efficiency.However,frequency drift is very common in UHF RFID systems,and will have an influence on the recovery on the physical layer.To address the problem of recovery with the frequency drift,this paper adopts a radial basis function(RBF)network to separate the collision signals,and decode the signals via FM0 to recovery collided RFID tags.Numerical results show that the method in this paper has better performance of symbol error rate(SER)and separation efficiency compared to conventional methods when frequency drift occurs.展开更多
Dynamic framed slotted Aloha algorithm is one of popular passive radio frequency identification(RFID) tag anticollision algorithms. In the algorithm, a frame length requires dynamical adjustment to achieve higher iden...Dynamic framed slotted Aloha algorithm is one of popular passive radio frequency identification(RFID) tag anticollision algorithms. In the algorithm, a frame length requires dynamical adjustment to achieve higher identification efficiency.Generally, the adjustment of the frame length is not only related to the number of tags, but also to the occurrence probability of capture effect. Existing algorithms could estimate both the number of tags and the probability of capture effect. Under large-scale RFID tag identification, however, the number of tags would be much larger than an initial frame length. In this scenario, the existing algorithm's estimation errors would substantially increase. In this paper, we propose a novel algorithm called capture-aware Bayesian estimate, which adopts Bayesian rules to accurately estimate the number and the probability simultaneously. From numerical results, the proposed algorithm adapts well to the large-scale RFID tag identification. It has lower estimation errors than the existing algorithms. Further,the identification efficiency from the proposed estimate is also higher than the existing algorithms.展开更多
In this paper a new active RFID system at 2.45 GHz based on the low-power system-on-chip CC2530 RF transceiver is designed and implemented. Only by using of an integrated multi-channel fast chip, both the MCU and RF o...In this paper a new active RFID system at 2.45 GHz based on the low-power system-on-chip CC2530 RF transceiver is designed and implemented. Only by using of an integrated multi-channel fast chip, both the MCU and RF operations are done which makes the RFID more reliable and reduces the complexity of the hardware and cost, vividly. This RFID system utilizes the Zig-Bee IEEE 802.15.4 standard in the ISM band. A lot amount of energy is restored by setting Tags in the sleep mode in the most of times. The maximum transmission range of 80 m at the output power of 4.5 dBm is obtained. The main application of this system is for the container identification with precise operation and high accuracy. An active Tag with unique ID is mounted on each vehicle. By enabling the AUTOCRC error detection possibility, minor errors are detected in the received frames. Receiver sensitivity of –97 dBm and current consumption of 1 μA in the sleep mode and 29.6 mA in the active mode are reported.展开更多
针对目前RFID(Radio Frequency Identification,射频识别技术)系统安全分析中忽略攻击事件对系统安全状态动态影响的问题,为了有效实现RFID系统的安全风险评估,文章提出了一种基于贝叶斯攻击图的RFID系统安全评估模型。该模型首先通过对...针对目前RFID(Radio Frequency Identification,射频识别技术)系统安全分析中忽略攻击事件对系统安全状态动态影响的问题,为了有效实现RFID系统的安全风险评估,文章提出了一种基于贝叶斯攻击图的RFID系统安全评估模型。该模型首先通过对RFID系统结构、所用协议进行分析确定系统的脆弱性漏洞及其依赖关系,建立攻击图。针对攻击图模型只能进行定性分析的问题,构建出相应的攻击图模型结构后可以结合贝叶斯理论对其进行量化。依据漏洞的利用难易度和影响程度建立RFID漏洞量化评价指标,计算出对应的原子攻击概率,然后以条件转移概率的形式将攻击节点与RFID系统的安全属性节点联系在一起,不仅能推断攻击者能够成功到达各个属性节点的风险概率,而且能够依据攻击者的不同行为动态展示系统风险状况的变化,实现评估不同状态下目标RFID系统的整体风险状况。实验表明,所提模型可以有效地计算出RFID系统整体的风险概率,为后续实施对应的安全策略提供理论依据。展开更多
基金supported by the National Natural Science Foundation of China(No.62027801).
文摘This study presents a radio frequency(RF)fingerprint identification method combining a convolutional neural network(CNN)and gated recurrent unit(GRU)network to identify measurement and control signals.The proposed algorithm(CNN-GRU)uses a convolutional layer to extract the IQ-related learning timing features.A GRU network extracts timing features at a deeper level before outputting the final identification results.The number of parameters and the algorithm’s complexity are reduced by optimizing the convolutional layer structure and replacing multiple fully-connected layers with gated cyclic units.Simulation experiments show that the algorithm achieves an average identification accuracy of 84.74% at a -10 dB to 20 dB signal-to-noise ratio(SNR)with fewer parameters and less computation than a network model with the same identification rate in a software radio dataset containing multiple USRP X310s from the same manufacturer,with fewer parameters and less computation than a network model with the same identification rate.The algorithm is used to identify measurement and control signals and ensure the security of the measurement and control link with theoretical and engineering applications.
基金Supported by the Project of the National "948" (2006-Z12)
文摘As a kind of brand-new technology, radio frequency identification management, data control and acquisition. This paper introduced food safety system construction, analyzed the advantages and problems in dairy modem suggestions for solution according to the practical situation. (RFID) plays an important role in dairy information tracing and culture function extension of managing breeding technology, and finally put forward some
基金The authors would like to thank the reviewers for their detailed reviews and constructive comments, which have helped improve the quality of this paper. This paper is supported by the National Natural Science Founda- tion of China (No. 61371092), the Doctoral Fund of Ministry of Education of China (No.20130061120062), and the China Postdoc- toral Science Foundation (No. 2014M551184).
文摘Radio frequency identification(RFID) is a new type of non-contact automatic identification technology.Due to its low energy consumption,low cost,and its adaptability to harsh environments,it has been applied to many fields.In the RFID systems,data collision is inevitable when the reader sends a communication request and multiple tags respond with simultaneous data transmission.Data collision is prone to causing problems such as:identification delay,spectrum resource waste,a decreased system throughput rate,etc.Therefore,an efficient,stable anti-collision protocol is crucial for RFID systems.This research analysed the current research into RFID anticollision protocols and summarised means for its improvement through the mechanism of implementation of different types anticollision protocols.Finally,a new direction is proposed for the future development of RFID anti-collision protocol systems.
基金National Natural Science Foundation of China(No.71301038)
文摘In order to construct a resource-saving and environmentfriendly society,the advantages of radio frequency identification(RFID) were considered.And it put forward the idea of introducing RFID in the recycling activities of waste products.Taking into account such elements as the technical level of RFID,cost saving from remanufacturing and the cost of RFID tags,both centralized and decentralized supply chain models with different participants in waste product collection were created,in order to determine the optimal pricing strategy and RFID technical level.In the end,sensitivity analyses were conducted to analyze the impacts of scaling parameter for additional increased recovery rate with RFID on pricing and RFID technology level,and impacts of cost saving on the profits of participants in different remanufacturing closed-loop supply chain models.
基金supported by the National Natural Science Foundation of China(62061003)Sichuan Science and Technology Program(2021YFG0192)the Research Foundation of the Civil Aviation Flight University of China(ZJ2020-04,J2020-033)。
文摘Existing specific emitter identification(SEI)methods based on hand-crafted features have drawbacks of losing feature information and involving multiple processing stages,which reduce the identification accuracy of emitters and complicate the procedures of identification.In this paper,we propose a deep SEI approach via multidimensional feature extraction for radio frequency fingerprints(RFFs),namely,RFFsNet-SEI.Particularly,we extract multidimensional physical RFFs from the received signal by virtue of variational mode decomposition(VMD)and Hilbert transform(HT).The physical RFFs and I-Q data are formed into the balanced-RFFs,which are then used to train RFFsNet-SEI.As introducing model-aided RFFs into neural network,the hybrid-driven scheme including physical features and I-Q data is constructed.It improves physical interpretability of RFFsNet-SEI.Meanwhile,since RFFsNet-SEI identifies individual of emitters from received raw data in end-to-end,it accelerates SEI implementation and simplifies procedures of identification.Moreover,as the temporal features and spectral features of the received signal are both extracted by RFFsNet-SEI,identification accuracy is improved.Finally,we compare RFFsNet-SEI with the counterparts in terms of identification accuracy,computational complexity,and prediction speed.Experimental results illustrate that the proposed method outperforms the counterparts on the basis of simulation dataset and real dataset collected in the anechoic chamber.
基金supported by the National Natural Science Foundation of China(61762093)the 17th Batches of Young and Middle-aged Leaders in Academic and Technical Reserved Talents Project of Yunnan Province(2014HB019)+1 种基金the Key Applied and Basic Research Foundation of Yunnan Province(2018FA036)the Program for Innovative Research Team(in Science and Technology)in University of Yunnan Province。
文摘In a passive ultra-high frequency(UHF)radio frequency identification(RFID)system,the recovery of collided tag signals on a physical layer can enhance identification efficiency.However,frequency drift is very common in UHF RFID systems,and will have an influence on the recovery on the physical layer.To address the problem of recovery with the frequency drift,this paper adopts a radial basis function(RBF)network to separate the collision signals,and decode the signals via FM0 to recovery collided RFID tags.Numerical results show that the method in this paper has better performance of symbol error rate(SER)and separation efficiency compared to conventional methods when frequency drift occurs.
基金supported in part by the National Natural Science Foundation of China(61762093)the 17th Batch of Young and Middle-aged Leaders in Academic and Technical Reserved Talents Project of Yunnan Province(2014HB019)the Program for Innovative Research Team(in Science and Technology)in University of Yunnan Province
文摘Dynamic framed slotted Aloha algorithm is one of popular passive radio frequency identification(RFID) tag anticollision algorithms. In the algorithm, a frame length requires dynamical adjustment to achieve higher identification efficiency.Generally, the adjustment of the frame length is not only related to the number of tags, but also to the occurrence probability of capture effect. Existing algorithms could estimate both the number of tags and the probability of capture effect. Under large-scale RFID tag identification, however, the number of tags would be much larger than an initial frame length. In this scenario, the existing algorithm's estimation errors would substantially increase. In this paper, we propose a novel algorithm called capture-aware Bayesian estimate, which adopts Bayesian rules to accurately estimate the number and the probability simultaneously. From numerical results, the proposed algorithm adapts well to the large-scale RFID tag identification. It has lower estimation errors than the existing algorithms. Further,the identification efficiency from the proposed estimate is also higher than the existing algorithms.
文摘In this paper a new active RFID system at 2.45 GHz based on the low-power system-on-chip CC2530 RF transceiver is designed and implemented. Only by using of an integrated multi-channel fast chip, both the MCU and RF operations are done which makes the RFID more reliable and reduces the complexity of the hardware and cost, vividly. This RFID system utilizes the Zig-Bee IEEE 802.15.4 standard in the ISM band. A lot amount of energy is restored by setting Tags in the sleep mode in the most of times. The maximum transmission range of 80 m at the output power of 4.5 dBm is obtained. The main application of this system is for the container identification with precise operation and high accuracy. An active Tag with unique ID is mounted on each vehicle. By enabling the AUTOCRC error detection possibility, minor errors are detected in the received frames. Receiver sensitivity of –97 dBm and current consumption of 1 μA in the sleep mode and 29.6 mA in the active mode are reported.
文摘针对目前RFID(Radio Frequency Identification,射频识别技术)系统安全分析中忽略攻击事件对系统安全状态动态影响的问题,为了有效实现RFID系统的安全风险评估,文章提出了一种基于贝叶斯攻击图的RFID系统安全评估模型。该模型首先通过对RFID系统结构、所用协议进行分析确定系统的脆弱性漏洞及其依赖关系,建立攻击图。针对攻击图模型只能进行定性分析的问题,构建出相应的攻击图模型结构后可以结合贝叶斯理论对其进行量化。依据漏洞的利用难易度和影响程度建立RFID漏洞量化评价指标,计算出对应的原子攻击概率,然后以条件转移概率的形式将攻击节点与RFID系统的安全属性节点联系在一起,不仅能推断攻击者能够成功到达各个属性节点的风险概率,而且能够依据攻击者的不同行为动态展示系统风险状况的变化,实现评估不同状态下目标RFID系统的整体风险状况。实验表明,所提模型可以有效地计算出RFID系统整体的风险概率,为后续实施对应的安全策略提供理论依据。