Journal articles: 'Radio Frequency Fingerprint'

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Deng, Shouyun, Zhitao Huang, Xiang Wang, and Guangquan Huang. "Radio Frequency Fingerprint Extraction Based on Multidimension Permutation Entropy." International Journal of Antennas and Propagation 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/1538728.

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Radio frequency fingerprint (RF fingerprint) extraction is a technology that can identify the unique radio transmitter at the physical level, using only external feature measurements to match the feature library. RF fingerprint is the reflection of differences between hardware components of transmitters, and it contains rich nonlinear characteristics of internal components within transmitter. RF fingerprint technique has been widely applied to enhance the security of radio frequency communication. In this paper, we propose a new RF fingerprint method based on multidimension permutation entropy. We analyze the generation mechanism of RF fingerprint according to physical structure of radio transmitter. A signal acquisition system is designed to capture the signals to evaluate our method, where signals are generated from the same three Anykey AKDS700 radios. The proposed method can achieve higher classification accuracy than that of the other two steady-state methods, and its performance under different SNR is evaluated from experimental data. The results demonstrate the effectiveness of the proposal.

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Zhang, Yulan, Jun Hu, Rundong Jiang, Zengrong Lin, and Zengping Chen. "Fine-Grained Radio Frequency Fingerprint Recognition Network Based on Attention Mechanism." Entropy 26, no. 1 (December 27, 2023): 29. http://dx.doi.org/10.3390/e26010029.

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With the rapid development of the internet of things (IoT), hundreds of millions of IoT devices, such as smart home appliances, intelligent-connected vehicles, and wearable devices, have been connected to the network. The open nature of IoT makes it vulnerable to cybersecurity threats. Traditional cryptography-based encryption methods are not suitable for IoT due to their complexity and high communication overhead requirements. By contrast, RF-fingerprint-based recognition is promising because it is rooted in the inherent non-reproducible hardware defects of the transmitter. However, it still faces the challenges of low inter-class variation and large intra-class variation among RF fingerprints. Inspired by fine-grained recognition in computer vision, we propose a fine-grained RF fingerprint recognition network (FGRFNet) in this article. The network consists of a top-down feature pathway hierarchy to generate pyramidal features, attention modules to locate discriminative regions, and a fusion module to adaptively integrate features from different scales. Experiments demonstrate that the proposed FGRFNet achieves recognition accuracies of 89.8% on 100 ADS-B devices, 99.5% on 54 Zigbee devices, and 83.0% on 25 LoRa devices.

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Shen, Danyao, Fengchao Zhu, Zhanpeng Zhang, and Xiaodong Mu. "Radio Frequency Fingerprint Identification Based on Metric Learning." International Journal of Information Technologies and Systems Approach 16, no. 3 (April 13, 2023): 1–13. http://dx.doi.org/10.4018/ijitsa.321194.

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With the popularization of the internet of things (IoT), its security has become increasingly prominent. Radio-frequency fingerprinting (RFF) is used as a physical-layer security method to provide security in wireless networks. However, the problems of poor performance in a highly noisy environment and less consideration of calculation resources are urgent to be resolved in a practical RFF application domain. The authors propose a new RFF identification method based on metric learning. They used power spectrum density (PSD) to extract the RFF from the nonlinearity of the RF front end. Then they adopted the large margin nearest neighbor (LMNN) classification algorithm to identify eight software-defined radio (SDR) devices. Different from existing RFF identification algorithms, the proposed LMNN method is more general and can learn the optimal metric from the wireless communication environment. Furthermore, they propose a new training and test strategy based on mixed SNR, which significantly improves the performance of conventional low-complexity RFF identification methods. Experimental results show that the proposed method can achieve 99.8% identification accuracy with 30dB SNR and 96.83% with 10dB SNR. In conclusion, the study demonstrates the effectiveness of the proposed method in recognition efficiency and computational complexity.

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Sun, Haotai, Xiaodong Zhu, Yuanning Liu, and Wentao Liu. "Construction of Hybrid Dual Radio Frequency RSSI (HDRF-RSSI) Fingerprint Database and Indoor Location Method." Sensors 20, no. 10 (May 24, 2020): 2981. http://dx.doi.org/10.3390/s20102981.

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Radio frequency communication technology has not only greatly improved public network service, but also developed a new technological route for indoor navigation service. However, there is a gap between the precision and accuracy of indoor navigation services provided by indoor navigation service and the expectation of the public. This study proposed a method for constructing a hybrid dual frequency received signal strength indicator (HDRF-RSSI) fingerprint library, which is different from the traditional RSSI fingerprint library constructing method in indoor space using 2.4G radio frequency (RF) under the same Wi-Fi infrastructure condition. The proposed method combined 2.4G RF and 5G RF on the same access point (AP) device to construct a HDRF-RSSI fingerprint library, thereby doubling the fingerprint dimension of each reference point (RP). Experimental results show that the feature discriminability of HDRF-RSSI fingerprinting is 18.1% higher than 2.4G RF RSSI fingerprinting. Moreover, the hybrid radio frequency fingerprinting model, training loss function, and location evaluation algorithm based on the machine learning method were designed, so as to avoid limitation that transmission point (TP) and AP must be visible in the positioning method. In order to verify the effect of the proposed HDRF-RSSI fingerprint library construction method and the location evaluation algorithm, dual RF RSSI fingerprint data was collected to construct a fingerprint library in the experimental scene, which was trained using the proposed method. Several comparative experiments were designed to compare the positioning performance indicators such as precision and accuracy. Experimental results demonstrate that compared with the existing machine learning method based on Wi-Fi 2.4G RF RSSI fingerprint, the machine learning method combining Wi-Fi 5G RF RSSI vector and the original 2.4G RF RSSI vector can effectively improve the precision and accuracy of indoor positioning of the smart phone.

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zhuo, Fei, Yuanling Huang, and Jian chen. "Radio Frequency Fingerprint Extraction of Radio Emitter Based on I/Q Imbalance." Procedia Computer Science 107 (2017): 472–77. http://dx.doi.org/10.1016/j.procs.2017.03.092.

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Rehman, Saeed Ur, Shafiq Alam, and Iman T. Ardekani. "An Overview of Radio Frequency Fingerprinting for Low-End Devices." International Journal of Mobile Computing and Multimedia Communications 6, no. 3 (July 2014): 1–21. http://dx.doi.org/10.4018/ijmcmc.2014070101.

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RF fingerprinting is proposed as a means of providing an additional layer of security for wireless devices. A masquerading or impersonation attacks can be prevented by establishing the identity of wireless transmitter using unique transmitter RF fingerprint. Unique RF fingerprints are attributable to the analog components (digital-to-analog converters, band-pass filters, frequency mixers and power amplifiers) present in the RF front ends of transmitters. Most of the previous researches have reported promising results with an accuracy of up to 99% using high-end receivers (e.g. Giga-sampling rate oscilloscopes, spectrum and vector signal analysers) to validate the proposed techniques. However, practical implementation of RF fingerprinting would require validation with low-end (low-cost) devices that also suffers from impairments due to the presence of analog components in the front end of its receiver. This articles provides the analysis and implementation of RF fingerprinting using low-cost receivers and challenges associated with it.

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Shen, Guanxiong, Junqing Zhang, Alan Marshall, Linning Peng, and Xianbin Wang. "Radio Frequency Fingerprint Identification for LoRa Using Deep Learning." IEEE Journal on Selected Areas in Communications 39, no. 8 (August 2021): 2604–16. http://dx.doi.org/10.1109/jsac.2021.3087250.

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Chang, Jiale, Zhengxiao Zhou, Siya Mi, and Yu Zhang. "Radio frequency fingerprint recognition method based on prior information." Computers and Electrical Engineering 120 (December 2024): 109684. http://dx.doi.org/10.1016/j.compeleceng.2024.109684.

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Htun, Myo Thet. "Compact and Robust MFCC-based Space-Saving Audio Fingerprint Extraction for Efficient Music Identification on FM Broadcast Monitoring." Journal of ICT Research and Applications 16, no. 3 (December 27, 2022): 226–42. http://dx.doi.org/10.5614/itbj.ict.res.appl.2022.16.3.3.

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The Myanmar music industry urgently needs an efficient broadcast monitoring system to solve copyright infringement issues and illegal benefit-sharing between artists and broadcasting stations. In this paper, a broadcast monitoring system is proposed for Myanmar FM radio stations by utilizing space-saving audio fingerprint extraction based on the Mel Frequency Cepstral Coefficient (MFCC). This study focused on reducing the memory requirement for fingerprint storage while preserving the robustness of the audio fingerprints to common distortions such as compression, noise addition, etc. In this system, a three-second audio clip is represented by a 2,712-bit fingerprint block. This significantly reduces the memory requirement when compared to Philips Robust Hashing (PRH), one of the dominant audio fingerprinting methods, where a three-second audio clip is represented by an 8,192-bit fingerprint block. The proposed system is easy to implement and achieves correct and speedy music identification even on noisy and distorted broadcast audio streams. In this research work, we deployed an audio fingerprint database of 7,094 songs and broadcast audio streams of four local FM channels in Myanmar to evaluate the performance of the proposed system. The experimental results showed that the system achieved reliable performance.

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Zhang, Junqing, Roger Woods, Magnus Sandell, Mikko Valkama, Alan Marshall, and Joseph Cavallaro. "Radio Frequency Fingerprint Identification for Narrowband Systems, Modelling and Classification." IEEE Transactions on Information Forensics and Security 16 (2021): 3974–87. http://dx.doi.org/10.1109/tifs.2021.3088008.

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Malathy, V., N. Shilpa, M. Anand, and R. Elavarasi. "Radio frequency identification based electronic voting machine using fingerprint module." IOP Conference Series: Materials Science and Engineering 981 (December 5, 2020): 032018. http://dx.doi.org/10.1088/1757-899x/981/3/032018.

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Wang, Shenhua, Hongliang Jiang, Xiaofang Fang, Yulong Ying, Jingchao Li, and Bin Zhang. "Radio Frequency Fingerprint Identification Based on Deep Complex Residual Network." IEEE Access 8 (2020): 204417–24. http://dx.doi.org/10.1109/access.2020.3037206.

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Sun, Liting, Xiang Wang, Afeng Yang, and Zhitao Huang. "Radio Frequency Fingerprint Extraction Based on Multi-Dimension Approximate Entropy." IEEE Signal Processing Letters 27 (2020): 471–75. http://dx.doi.org/10.1109/lsp.2020.2978333.

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Kang, Jusung, Younghak Shin, Hyunku Lee, Jintae Park, and Heungno Lee. "Radio Frequency Fingerprinting for Frequency Hopping Emitter Identification." Applied Sciences 11, no. 22 (November 16, 2021): 10812. http://dx.doi.org/10.3390/app112210812.

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In a frequency hopping spread spectrum (FHSS) network, the hopping pattern plays an important role in user authentication at the physical layer. However, recently, it has been possible to trace the hopping pattern through a blind estimation method for frequency hopping (FH) signals. If the hopping pattern can be reproduced, the attacker can imitate the FH signal and send the fake data to the FHSS system. To prevent this situation, a non-replicable authentication system that targets the physical layer of an FHSS network is required. In this study, a radio frequency fingerprinting-based emitter identification method targeting FH signals was proposed. A signal fingerprint (SF) was extracted and transformed into a spectrogram representing the time–frequency behavior of the SF. This spectrogram was trained on a deep inception network-based classifier, and an ensemble approach utilizing the multimodality of the SFs was applied. A detection algorithm was applied to the output vectors of the ensemble classifier for attacker detection. The results showed that the SF spectrogram can be effectively utilized to identify the emitter with 97% accuracy, and the output vectors of the classifier can be effectively utilized to detect the attacker with an area under the receiver operating characteristic curve of 0.99.

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Chen, Siji, Bin Shen, Xin Wang, and Sang-Jo Yoo. "Geo-Location Information Aided Spectrum Sensing in Cellular Cognitive Radio Networks." Sensors 20, no. 1 (December 30, 2019): 213. http://dx.doi.org/10.3390/s20010213.

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Apart from the received signal energy, auxiliary information plays an important role in remarkably ameliorating conventional spectrum sensing. In this paper, a novel spectrum sensing scheme aided by geolocation information is proposed. In the cellular cognitive radio network (CCRN), secondary user equipments (SUEs) first acquire their wireless fingerprints via either received signal strength (RSS) or time of arrival (TOA) estimation over the reference signals received from their surrounding base-stations (BSs) and then pinpoint their geographical locations through a wireless fingerprint (WFP) matching process in the wireless fingerprint database (WFPD). Driven by the WFPD, the SUEs can easily ascertain for themselves the white licensed frequency band (LFB) for opportunistic access. In view of the fact that the locations of the primary user (PU) transmitters in the CCRN are either readily known or practically unavailable, the SUEs can either search the WFPD directly or rely on the support vector machine (SVM) algorithm to determine the availability of the LFB. Additionally, in order to alleviate the deficiency of single SUE-based sensing, a joint prediction mechanism is proposed on the basis of cooperation of multiple SUEs that are geographically nearby. Simulations verify that the proposed scheme achieves higher detection probability and demands less energy consumption than the conventional spectrum sensing algorithms.

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Chen, Tianshu, Aiqun Hu, and Yu Jiang. "Radio Frequency Fingerprint-Based DSRC Intelligent Vehicle Networking Identification Mechanism in High Mobility Environment." Sustainability 14, no. 9 (April 22, 2022): 5037. http://dx.doi.org/10.3390/su14095037.

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In recent years, Dedicated Short-Range Communication (DSRC) vehicle interconnection technology has achieved mature development and broad applications, which is the key Vehicle to Everything (V2X) technology to realize transport intelligence. However, the openness of wireless transmission and the mobility of wireless terminals cause the identification mechanism of the DSRC system to face serious security threats. A radio frequency fingerprint (RFF)-based identification method can better resist the identity attack and spoofing by extracting the hardware characteristics formed by the differences of electronic components to authenticate different devices. Therefore, in this paper a novel RFF identification mechanism is proposed for IEEE 802.11p protocol-based DSRC intelligent vehicle networking devices suitable for a high mobility environment, in which the preamble field features of physical layer frames are extracted as device fingerprints, and the random forest algorithm and sequential detection method are used to distinguish and authenticate different devices. The experiment and simulation results demonstrate that the identification accuracy rates of the eight DSRC modules in the low-speed LOS and NLOS experimental states and up to 70 km/h high-speed simulations all exceed 99%, illustrating that this method has important application value in the field of identity authentication of V2X devices in high-speed scenarios.

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Zhu, Qingchao, Qiqi Guo, Xiaoou Song, and Yue Zhang. "Research on combined radio frequency fingerprint identification model with limited samples." Journal of Physics: Conference Series 2284, no. 1 (June 1, 2022): 012014. http://dx.doi.org/10.1088/1742-6596/2284/1/012014.

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Abstract Aiming to solve the real problem of civilian aircraft identification, a novel combined radio frequency fingerprint (RFF) identification model is proposed, consisting of data analyzing processing, standard characteristic parameter database establishment, classification and optimization. In data analyzing processing step, discrimination was realized for wavelet coefficients, instantaneous phase, Hilbert huang transform energy spectrum, coefficients, time field envelope, probability density function, on basis of which, characteristic parameters were confirmed. In standard characteristic parameter database establishment step, a standard database was found through direct measurement method to avoid losing the RFF feature. In classification step, single character assortment rule and combined classifying rule were defined, with correlative concept and threshold concept. Finally, optimization for the model was realized by modifying parameters manually. Results show that, though hardware was limited and amount of samples were fewer, average identification rate is near to 69.75 percent, providing a theoretical reference for the real problem of identifying different aircrafts.

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Zhou, Xinyu, Aiqun Hu, Guyue Li, Linning Peng, Yuexiu Xing, and Jiabao Yu. "A Robust Radio-Frequency Fingerprint Extraction Scheme for Practical Device Recognition." IEEE Internet of Things Journal 8, no. 14 (July 15, 2021): 11276–89. http://dx.doi.org/10.1109/jiot.2021.3051402.

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Shen, Guanxiong, Junqing Zhang, Alan Marshall, and Joseph R. Cavallaro. "Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa." IEEE Transactions on Information Forensics and Security 17 (2022): 774–87. http://dx.doi.org/10.1109/tifs.2022.3152404.

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Lu, Jianyin. "A New Indoor Location Algorithm Based on Radio Frequency Fingerprint Matching." IEEE Access 8 (2020): 83290–97. http://dx.doi.org/10.1109/access.2020.2989137.

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Gong, Jialiang, Xiaodong Xu, and Yingke Lei. "Unsupervised Specific Emitter Identification Method Using Radio-Frequency Fingerprint Embedded InfoGAN." IEEE Transactions on Information Forensics and Security 15 (2020): 2898–913. http://dx.doi.org/10.1109/tifs.2020.2978620.

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Nah, Sun-Phil, Jun-Il Ahn, and Sung-Jin Jo. "Implementation of Radio Frequency Fingerprint Identification System using Compressed Sensing Receiver." Journal of Korean Institute of Information Technology 21, no. 9 (September 30, 2023): 1–10. http://dx.doi.org/10.14801/jkiit.2023.21.9.1.

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Tian, Qiao, Yun Lin, Xinghao Guo, Jin Wang, Osama AlFarraj, and Amr Tolba. "An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology." Sensors 20, no. 4 (February 22, 2020): 1213. http://dx.doi.org/10.3390/s20041213.

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With the continuous development of science and engineering technology, our society has entered the era of the mobile Internet of Things (MIoT). MIoT refers to the combination of advanced manufacturing technologies with the Internet of Things (IoT) to create a flexible digital manufacturing ecosystem. The wireless communication technology in the Internet of Things is a bridge between mobile devices. Therefore, the introduction of machine learning (ML) algorithms into MIoT wireless communication has become a research direction of concern. However, the traditional key-based wireless communication method demonstrates security problems and cannot meet the security requirements of the MIoT. Based on the research on the communication of the physical layer and the support vector data description (SVDD) algorithm, this paper establishes a radio frequency fingerprint (RFF or RF fingerprint) authentication model for a communication device. The communication device in the MIoT is accurately and efficiently identified by extracting the radio frequency fingerprint of the communication signal. In the simulation experiment, this paper introduces the neighborhood component analysis (NCA) method and the SVDD method to establish a communication device authentication model. At a signal-to-noise ratio (SNR) of 15 dB, the authentic devices authentication success rate (ASR) and the rogue devices detection success rate (RSR) are both 90%.

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Tian, Yuan, Hong Wen, Jiaxin Zhou, Zhiqiang Duan, and Tao Li. "Optimized Radio Frequency Footprint Identification Based on UAV Telemetry Radios." Sensors 24, no. 16 (August 6, 2024): 5099. http://dx.doi.org/10.3390/s24165099.

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With the widespread use of unmanned aerial vehicles (UAVs), the detection and identification of UAVs is a vital security issue for the safety of airspace and ground facilities in the no-fly zone. Telemetry radios are important wireless communication devices for UAVs, especially in UAVs beyond the visual line of sight (BVLOS) operating mode. This work focuses on the UAV identification approach using transient signals from UAV telemetry radios instead of the signals from UAV controllers that the former research work depended on. In our novel UAV Radio Frequency (RF) identification system framework based on telemetry radio signals, the EC−α algorithm is optimized to detect the starting point of the UAV transient signal and the detection accuracy at different signal-to-noise ratios (SNR) is evaluated. In the training stage, the Convolutional Neural Network (CNN) model is trained to extract features from raw I/Q data of the transient signals with different waveforms. Its architecture and hyperparameters are analyzed and optimized. In the identification stage, the extracted transient signals are clustered through the Self-Organizing Map (SOM) algorithm and the Clustering Signals Joint Identification (CSJI) algorithm is proposed to improve the accuracy of RF fingerprint identification. To evaluate the performance of our proposed approach, we design a testbed, including two UAVs as the flight platform, a Universal Software Radio Peripheral (USRP) as the receiver, and 20 telemetry radios with the same model as targets for identification. Indoor test results show that the optimized identification approach achieves an average accuracy of 92.3% at 30 dB. In comparison, the identification accuracy of SVM and KNN is 69.7% and 74.5%, respectively, at the same SNR condition. Extensive experiments are conducted outdoors to demonstrate the feasibility of this approach.

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Peng, Yitang, Xiaoji Niu, Jian Tang, Dazhi Mao, and Chuang Qian. "Fast Signals of Opportunity Fingerprint Database Maintenance with Autonomous Unmanned Ground Vehicle for Indoor Positioning." Sensors 18, no. 10 (October 12, 2018): 3419. http://dx.doi.org/10.3390/s18103419.

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Indoor positioning technology based on Received Signal Strength Indicator (RSSI) fingerprints is a potential navigation solution, which has the advantages of simple implementation, low cost and high precision. However, as the radio frequency signals can be easily affected by the environmental change during its transmission, it is quite necessary to build location fingerprint database in advance and update it frequently, thereby guaranteeing the positioning accuracy. At present, the fingerprint database building methods mainly include point collection and line acquisition, both of which are usually labor-intensive and time consuming, especially in a large map area. This paper proposes a fast and efficient location fingerprint database construction and updating method based on a self-developed Unmanned Ground Vehicle (UGV) platform NAVIS, called Automatic Robot Line Collection. A smartphone was installed on NAVIS for collecting indoor Received Signal Strength Indicator (RSSI) fingerprints of Signals of Opportunity (SOP), such as Bluetooth and Wi-Fi. Meanwhile, indoor map was created by 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) technology. The UGV automatically traverse the unknown indoor environment due to a pre-designed full-coverage path planning algorithm. Then, SOP sensors collect location fingerprints and generates grid map during the process of environment-traversing. Finally, location fingerprint database is built or updated by Kriging interpolation. Field tests were carried out to verify the effectiveness and efficiency of our proposed method. The results showed that, compared with the traditional point collection and line collection schemes, the root mean square error of the fingerprinting-based positioning results were reduced by 35.9% and 25.0% in static tests and 30.0% and 21.3% respectively in dynamic tests. Moreover, our UGV can traverse the indoor environment autonomously without human-labor on data acquisition, the efficiency of the automatic robot line collection scheme is 2.65 times and 1.72 times that of the traditional point collection and the traditional line acquisition, respectively.

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Yang, Tian, Su Hu, Weiwei Wu, Lixin Niu, Di Lin, and Jiabei Song. "Conventional Neural Network-Based Radio Frequency Fingerprint Identification Using Raw I/Q Data." Wireless Communications and Mobile Computing 2022 (August 22, 2022): 1–8. http://dx.doi.org/10.1155/2022/8681599.

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Radio frequency (RF) fingerprint identification is a nonpassword authentication method based on the physical layer of communication devices. Deep learning methods have thrown new light on RF fingerprint identification. In this paper, a conventional neural network- (CNN-) based RF identification model is proposed. The CNN models are designed to be lightweight. Raw data that reflects the characteristics of the I channel, the Q channel, and the 2-dimensional I + Q data is successively fed into a CNN model. Therefore, three submodels are generated. The final predictive labels are determined by the results of the three submodels through a voting scheme. Experimental results have demonstrated that in the SNR setting at 5 dB, the final recognition accuracy of four transmit devices could achieve as high as 97.25%, while the identification accuracies based on the I channel data, Q channel data, and I + Q channel data are 94.5%, 95%, and 94.5%, respectively. The training time for the 4 devices is around 30 seconds.

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P, Mrs Smitha. "Vehicle Theft Authentication System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 16, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem34089.

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This thesis presents a robust security system designed to tackle the escalating issue of vehicle theft and unlicensed driving. By integrating a suite of cutting-edge technologies including Arduino, driver's permit card (DL),radio-frequency ID scanner (RFID),biometric fingerprint sensor (FP), Face Recognition (FR), and Global System for Mobile Communication modem (GSM), the system aims to fortify vehicles against unauthorized access. At its core, Arduino serves as the central intelligence, capable of storing and processing authorized driver data such as facial recognition and fingerprint records. When a driver inserts their license into the RFID reader, the system cross-references the information with stored data. If the license is validated, the system proceeds to authenticate the driver's identity through facial recognition and fingerprint scanning. Successful verification enables the ignition system, granting access to the vehicle. However, any discrepancy triggers an immediate SMS alert via the GSM modem to the vehicle owner, while simultaneously disabling the ignition to thwart unauthorized use. Additionally, an alcohol detection module further enhances safety by preventing vehicle ignition if alcohol is detected in the driver's system. This comprehensive approach not only safeguards vehicles from theft but also promotes responsible driving practices, exemplified by timely license renewal reminders and the enforcement of alcohol-free driving. Key Words: Vehicle security system,Arduino integration,Driver's license card (DL), radio-frequency ID scanner (RFID),Fingerprint module (FP) Face Recognition (FR), GSM-enabled mobile communication device, Unauthorized access prevention,Facial recognition,Fingerprint scanning SMS alerts,Alcohol detection module,Responsible driving practices,License renewal reminders,Alcohol-free driving enforcement.

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ANSYAH, MUHAMMAD FARID ANDY THREE, and Slamet Winardi. "MESIN AKSES RUANGAN MENGGUNAKAN FINGERPRINT DAN RFID (RADIO FREQUENCY IDENTIFICATION) BERBASIS IOT (INTERNET OF THINGS)." Jurnal Pendidikan Teknologi Informasi (JUKANTI) 5, no. 1 (April 29, 2022): 58–68. http://dx.doi.org/10.37792/jukanti.v5i1.443.

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Teknologi informasi yang berkembang pesat membawa banyak manfaat untuk kehidupan masyarakat dalam berbagai bidang, salah satu pemanfaatan teknologi dalam bidang keamanan ruangan, akses utama dari ruangan adalah pintu, saat ini untuk keamanan pintu masih menggunakan kunci manual. Penelitian ini bertujuan untuk membuat mesin akses ruangan berbasis IOT (Internet Of Things) yang dapat dipantau melalui aplikasi berbasis android dan ios. Autentikasi utama untuk mengakses pintu ruangan menggunakan sensor fingerprint dan RFID, sehingga untuk mengakses ruangan hanya orang yang mempunyai sidik jari dan id card terdaftar pada database firebase. Untuk mendaftarkan sidik jari dan id card melalui aplikasi. Sensor fingerprint digunakan sebagai konfirmasi setelah RFID untuk menghindari jika id card ditemukan orang lain. Mikrokontroler yang digunakan adalah ESP32 Devkit V1. Sidik jari dan id card akan diproses mikrokontroler untuk dicocokkan dengan database, jika sidik jari dan id card sesuai dengan data yang ada pada database maka pintu ruangan akan terbuka dan riwayat akses dapat dilihat melalui aplikasi, sehingga orang yang sidik jari dan id card nya tidak terdaftar maka pintu ruangan tidak akan terbuka. Untuk kunci dari pintu ruangan menggunakan solenoid. Berdasarkan dari hasil pengujian dapat disimpulkan bahwa mesin akses ruangan menggunakan fingerprint dan RFID dapat bekerja dengan baik sesuai dengan rancangan.

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Li, Dongming, Zhaorui Wang, Yuting Lai, and Huafei Shen. "Dataset Augmentation and Fractional Frequency Offset Compensation Based Radio Frequency Fingerprint Identification in Drone Communications." Drones 8, no. 10 (October 10, 2024): 569. http://dx.doi.org/10.3390/drones8100569.

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The open nature of the wireless channel makes the drone communication vulnerable to adverse spoofing attacks, and the radio frequency fingerprint (RFF) identification is promising in effectively safeguarding the access security for drones. Since drones are constantly flying in the three dimensional aerial space, the unique RFF identification problem emerges in drone communication that the effective extraction and identification of RFF suffer from the time-varying channel effects and unavoidable jitterings due to the constant flight. To tackle this issue, we propose augmenting the training RFF dataset by regenerating the drone channel characteristics and compensate the fractional frequency offset. The proposed method estimates the Rician K value of the channel and curve-fits the statistical distribution, the Rician channels are regenerated using the sinusoidal superposition method. Then, a probabilistic switching channel is also set up to introduce the Rayleigh channel effects into the training dataset. The proposed method effectively addresses the unilateral channel effects in the training dataset and achieves the balanced channel effect distribution. Consequently, the pre-trained model can extract channel-robust RFF features in drone air-ground channels. In addition, by compensating the fractional frequency offset, the proposed method removes the unstable frequency components and retains the stable integer frequency offset. Then, the stable frequency offset features that are robust to environmental changes can be extracted. The proposed method achieves an average classification accuracy of 97% under spatial and temporal varying conditions.

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Li, Jingchao, Yulong Ying, Chunlei Ji, and Bin Zhang. "Differential Contour Stellar-Based Radio Frequency Fingerprint Identification for Internet of Things." IEEE Access 9 (2021): 53745–53. http://dx.doi.org/10.1109/access.2021.3071352.

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朱, 清超. "Design of a Combined Radio Frequency Fingerprint Identification Model with Limited Samples." Computer Science and Application 11, no. 10 (2021): 2459–77. http://dx.doi.org/10.12677/csa.2021.1110251.

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Xing, Yuexiu, Aiqun Hu, Junqing Zhang, Linning Peng, and Guyue Li. "On Radio Frequency Fingerprint Identification for DSSS Systems in Low SNR Scenarios." IEEE Communications Letters 22, no. 11 (November 2018): 2326–29. http://dx.doi.org/10.1109/lcomm.2018.2871454.

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Shao, Zhipeng, Zhuo Lv, Wengting Wang, and Tao Zhang. "Research on Illegal Mobile Device Identification Based on Radio Frequency Fingerprint Feature." Electronics 12, no. 14 (July 20, 2023): 3144. http://dx.doi.org/10.3390/electronics12143144.

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Internet of Things (IoT) technology is widely used in new power systems, and it also provides many new modes for network attacks. Illegal terminal device identification is also a significant topic in the field of wireless authentication technology. Some kinds of power network equipment are located in sparsely populated areas and rely on IoT terminals for real-time monitoring. Attackers use illegal terminals to connect power IoT devices for production monitoring and to carry out network attacks, which may cause serious damage, such as power data theft and misoperation of power network equipment. Radio frequency fingerprint (RFF) can extract hardware features from different devices, and is widely used for device identification and authentication. The area over which power network equipment placed is vast, and there are many wireless communication devices and terminals. It is difficult to identify illegal devices through commonly used network management techniques, thus making it difficult to distinguish between the mobile terminals of employees and illegal terminals in general spectrum screening. In response to the above situation, this paper uses the characteristics of the squared spectrum of random access preamble signals to extract hardware device features, proposes an illegal device identification algorithm based on Gaussian distribution theory, and evaluates its performance. The experimental results show that, when the signal-to-noise ratio (SNR) is greater than 15 dB, the average recognition result is greater than 92%. In addition, the algorithm has low computational complexity and high engineering application value.

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Huang, Renhui, Xinyong Peng, Zhi Chai, Mingye Li, Jiawei Ren, and Xuelin Yang. "Radio frequency fingerprint extraction and authentication towards open set in noisy channels." Digital Signal Processing 146 (March 2024): 104363. http://dx.doi.org/10.1016/j.dsp.2023.104363.

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Wei, JianYu, Lu Yu, Lei Zhu, and XingYu Zhou. "RF Fingerprint Extraction Method Based on CEEMDAN and Multidomain Joint Entropy." Wireless Communications and Mobile Computing 2022 (May 10, 2022): 1–16. http://dx.doi.org/10.1155/2022/5326892.

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Specific emitter identification (SEI) can distinguish communication radio emitters with the fingerprint features carried by the received signal, and this technology has been widely used in military and civilian fields. However, in the real electromagnetic environment, the number of communication radio emitters is large and the signal-to-noise ratio (SNR) is low, which leads to poor nonlinear fingerprint analysis of SEI in a single domain. Therefore, combining the exploration of multiple domains of electromagnetic spatial information resources, this paper proposed a radio frequency (RF) fingerprint extraction method based on complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and multidomain joint entropy. The proposed method is an attempt and exploration further extraction of nonlinear fingerprint features in multiple domains. Firstly, considering the nonstationarity of the communication signal, this article adopts the CEEMDAN method to decompose the signal to multiple intrinsic mode functions (IMF). Then, the decomposed signal is represented in multiple spaces by a multidimensional phase space reconstruction technique. Nonlinear analysis of the original signal is performed in multiple spaces: multidimensional differential approximate entropy space, singular spectral entropy space, and power spectral entropy space. Finally, the support vector machine (SVM) is adopted in the classification stage. To demonstrate the robustness of the method, the method is verified on the universal software radio peripheral (USRP) dataset and the Northeastern University public dataset. In terms of the identification accuracy, the proposed method performs with 98.5% accuracy on the 5-class USRP dataset. It also performs with 94.7% accuracy on the 16-class public dataset. The experimental results show that the proposed method has a stable identification performance and has a more than 85% recognition rate in the SNR above 5dB.

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Chen, Wen, Wu, Xu, Jiang, Song, and Chen. "Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication." Sensors 19, no. 16 (August 19, 2019): 3610. http://dx.doi.org/10.3390/s19163610.

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In this paper, a light-weight radio frequency fingerprinting identification (RFFID) scheme that combines with a two-layer model is proposed to realize authentications for a large number of resource-constrained terminals under the mobile edge computing (MEC) scenario without relying on encryption-based methods. In the first layer, signal collection, extraction of RF fingerprint features, dynamic feature database storage, and access authentication decision are carried out by the MEC devices. In the second layer, learning features, generating decision models, and implementing machine learning algorithms for recognition are performed by the remote cloud. By this means, the authentication rate can be improved by taking advantage of the machine-learning training methods and computing resource support of the cloud. Extensive simulations are performed under the IoT application scenario. The results show that the novel method can achieve higher recognition rate than that of traditional RFFID method by using wavelet feature effectively, which demonstrates the efficiency of our proposed method.

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Tian, Qiao, Yun Lin, Xinghao Guo, Jinming Wen, Yi Fang, Jonathan Rodriguez, and Shahid Mumtaz. "New Security Mechanisms of High-Reliability IoT Communication Based on Radio Frequency Fingerprint." IEEE Internet of Things Journal 6, no. 5 (October 2019): 7980–87. http://dx.doi.org/10.1109/jiot.2019.2913627.

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Xing, Yuexiu, Aiqun Hu, Junqing Zhang, Jiabao Yu, Guyue Li, and Ting Wang. "Design of a Robust Radio-Frequency Fingerprint Identification Scheme for Multimode LFM Radar." IEEE Internet of Things Journal 7, no. 10 (October 2020): 10581–93. http://dx.doi.org/10.1109/jiot.2020.3003692.

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Li, Xufei, Shuiguang Zeng, and Yangyang Liu. "Inter-Frame-Relationship Protected Signal: A New Design for Radio Frequency Fingerprint Authentication." Sensors 23, no. 15 (August 4, 2023): 6948. http://dx.doi.org/10.3390/s23156948.

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Utilizing a multi-frame signal (MFS) rather than a single-frame signal (SFS) for radio frequency fingerprint authentication (RFFA) shows the advantage of higher accuracy. However, previous studies have often overlooked the associated security threats in MFS-based RFFA. In this paper, we focus on the carrier-sense multiple access with collision avoidance channel and identify a potential security threat, in that an attacker may inject a forged frame into valid traffic, making it more likely to be accepted alongside legitimate frames. To counter such a security threat, we propose an innovative design called the inter-frame-relationship protected signal (IfrPS), which enables the receiver to determine whether two consecutively received frames originate from the same transmitter to safeguard the MFS-based RFFA. To demonstrate the applicability of our proposition, we analyze and numerically evaluate two important properties: its impact on message demodulation and the accuracy gain in IfrPS-aided, MFS-based RFFA compared with the SFS-based RFFA. Our results show that the proposed scheme has a minimal impact of only −0.5 dB on message demodulation, while achieving up to 5 dB gain for RFFA accuracy.

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Fu, Hua, Hao Dong, Jian Yin, and Linning Peng. "Radio Frequency Fingerprint Identification for 5G Mobile Devices Using DCTF and Deep Learning." Entropy 26, no. 1 (December 29, 2023): 38. http://dx.doi.org/10.3390/e26010038.

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The fifth-generation (5G) mobile cellular network is vulnerable to various security threats. Radio frequency fingerprint (RFF) identification is an emerging physical layer authentication technique which can be used to detect spoofing and distributed denial of service attacks. In this paper, the performance of RFF identification is studied for 5G mobile phones. The differential constellation trace figure (DCTF) is extracted from the physical random access channel (PRACH) preamble. When the database of all 64 PRACH preambles is available at the gNodeB (gNB), an index-based DCTF identification scheme is proposed, and the classification accuracy reaches 92.78% with a signal-to-noise ratio of 25 dB. Moreover, due to the randomness in the selection of preamble sequences in the random access procedure, when only a portion of the preamble sequences can be trained, a group-based DCTF identification scheme is proposed. The preamble sequences generated from the same root value are grouped together, and the untrained sequences can be identified based on the trained sequences within the same group. The classification accuracy of the group-based scheme is 89.59%. An experimental system has been set up using six 5G mobile phones of three models. The 5G gNB is implemented on the OpenAirInterface platform.

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Reddy, A. Vamshidhar, L. Yashwanth, L. Nithin, M. Sai Dinesh, and K. Ramya. "Fingerprint and RFID Based Bike and Car Ignition System." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (April 30, 2024): 2295–301. http://dx.doi.org/10.22214/ijraset.2024.60169.

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Abstract: This project deals with the issue in a more preferable way and ensures the safety of the vehicle. This system aims to create a simple biometric vehicle ignition and security system that protects the vehicle from unauthorized users. This system comprises of Arduino Uno along with EM18 RFID (Radio Frequency Identification) module and R305 Fingerprint sensor. The RFID module checks for the validity of the RFID tag code and permits to proceed further. Proceeding further the fingerprint sensor checks for the authenticity of the fingerprint and ignition takes place. Protecting of vehicles from thefts is important of individuals. Vehicle has ignition keys (normal keys) those can be easily cloned. There are some smart keys available with expensive prices. Smart keys can be copied using some technical loopholes. Here we want to design and develop vehicle access with RFID based license card and fingerprint access. This kind design can’t be cloned easily because of two level authentications. The proposed project title is car ignition control with fingerprint and RFID using Arduino Uno

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Blatnik, Aljaž, Luka Zmrzlak, and Boštjan Batagelj. "Radio Front-End for Frequency Agile Microwave Photonic Radars." Electronics 13, no. 23 (November 26, 2024): 4662. http://dx.doi.org/10.3390/electronics13234662.

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Recent advancements in photonic integrated circuits (PICs) have paved the way for a new era of frequency-agile coherent radar systems. Unlike traditional all-electronic RF radar techniques, fully photonic systems offer superior performance, overcoming bandwidth limitations and noise degradation when operating across S (2–4 GHz), X (8–12 GHz), and K-band (12–40 GHz) frequencies. They also exhibit excellent phase noise performance, even at frequencies exceeding 20 GHz. However, current state-of-the-art PICs still suffer from high processing losses in the optical domain, necessitating careful design of the electrical RF domain. This study delves into the critical challenges of designing RF front-ends for microwave photonic radars, including stability, noise minimization, and intermodulation distortion reduction. To demonstrate the feasibility of the proposed design, a functional prototype is constructed, achieving a total power gain of 107 dB (radar system at 10 GHz) while minimizing signal noise degradation. Furthermore, a comprehensive demonstration of the RF front-end, encompassing both optical RF signal generation and experimental measurements of a rotor blade’s Doppler fingerprint with 0.5 Hz resolution, validates the proposed system’s performance.

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Zhang, Liyang, Kunlei Liu, Zhiyou Pan, Lei Pan, Rui Gao, and Qian Zhang. "An Indoor Unknown Radio Emitter Positioning Approach Using Improved RSSD Location Fingerprinting." International Journal of Antennas and Propagation 2023 (February 28, 2023): 1–13. http://dx.doi.org/10.1155/2023/5462081.

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The accurate location of an unknown radio emitter (URE) is a critical task in wireless communication security. The URE localization method based on the received signal strength difference (RSSD) has become popular due to the identification of unknown transmitting power and frequency. However, high computational complexity and low positioning accuracy have been caused by the RSSD fingerprint data’s redundancy and cross-correlation. In this article, an indoor RSSD-based positioning algorithm combining principal component analysis (PCA) and Pearson correlation coefficient (PCC), called RSSD-PCA-PCC, is proposed to realize efficient feature extraction and reduce false fingerprint matching. Firstly, to achieve reduction and decorrelation, the principal components of the RSSD fingerprint database are extracted by the singular value decomposition (SVD) method. Secondly, the PCC is applied to measure the relative distance between the principal component features. In particular, the PCC is used for selecting the reference points (RPs) in order to match the position accurately. The results show that the proposed algorithm can obtain a more superior performance compared with the conventional RSSD-based weighted k-nearest neighbor algorithm (RSSD-WKNN) and COS matching algorithm (RSSD-PCA-COS) in the case of different selected RP numbers, AP numbers, and grid distances.

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Peng, Yang, Pengfei Liu, Yu Wang, Guan Gui, Bamidele Adebisi, and Haris Gacanin. "Radio Frequency Fingerprint Identification Based on Slice Integration Cooperation and Heat Constellation Trace Figure." IEEE Wireless Communications Letters 11, no. 3 (March 2022): 543–47. http://dx.doi.org/10.1109/lwc.2021.3135932.

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Park, Chan-Uk, and Yong-Hoon Choi. "Parallel Artificial Neural Network Learning Scheme Based on Radio Frequency Fingerprint for Indoor Localization." Journal of Korean Institute of Communications and Information Sciences 43, no. 6 (June 30, 2018): 979–85. http://dx.doi.org/10.7840/kics.2018.43.6.979.

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Gu, Xiaolin, Wenjia Wu, Aibo Song, Ming Yang, Zhen Ling, and Junzhou Luo. "RF-TESI: Radio Frequency Fingerprint-based Smartphone Identification under Temperature Variation." ACM Transactions on Sensor Networks, December 7, 2023. http://dx.doi.org/10.1145/3636462.

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Radio frequency fingerprint identification (RFFI) is a promising technique for smartphone identification. However, we find that the temperature of the RF front end in smartphones can significantly impact the RF features, including the carrier frequency offset (CFO) and statistical RF features. The unstable RF features caused by temperature changes can negatively affect the performance of state-of-the-art RFFI approaches. To this end, we propose the RF-TESI solution for smartphone identification under temperature variation. First, we construct a dataset by extracting temperature and RF features. In the dataset, the extracted temperature values constitute a set of temperature values and each registered temperature value corresponds to a group of RF features. Next, we evaluate the distinctiveness of RF features across smartphones to select the most suitable RF fingerprint. Then, we train multiple random forest models, each tagged with a registered temperature. In addition, because there are still many temperatures out of the temperature set, we design a RF fingerprint estimation method to estimate RF fingerprints at unregistered temperatures. Finally, the experiments show RF-TESI demonstrates satisfactory performance under different scenarios, taking into account variations in temperature, time and position. Besides, our proposed approach is better than all state-of-art approaches in smartphone identification.

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Liu, Mingqian, Chunheng Liu, Yunfei Chen, Zhiwen Yan, and Nan Zhao. "Radio Frequency Fingerprint Collaborative Intelligent Blind Identification for Green Radios." IEEE Transactions on Green Communications and Networking, 2022, 1. http://dx.doi.org/10.1109/tgcn.2022.3185045.

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Гребешков, А. Ю. "RESEARCH AND ANALYSIS RADIO FREQUENCY MACHINE LEARNING RFML." Электросвязь, no. 11(48) (November 29, 2023). http://dx.doi.org/10.34832/elsv.2023.48.11.012.

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Статья посвящена проблеме использования машинного обучения на радиочастотах в беспроводных сетях. Отмечается, что машинное обучение широко применяется в телекоммуникациях для решения задач классификации сигналов и причин отказов, прогнозирования событий с помощью, например, нейронных сетей. Имеется техническая возможность идентификации передатчика по радиоотпечатку на физическом уровне. Указывается на возможность использования машинного обучения на радиочастотах для построения классификатора радиоотпечатков радиоэлектронных средств. Приводятся практические примеры использования машинного обучения на радиочастотах. The paper is devoted to the problem of using radio frequency machine learning for wireless networks. It is noted that machine learning is widely used in telecommunications for signaling and fault classification and event prediction process with neuron networks as example. It is technically possible to identify a transmitter by radio fingerprint at the physical level. It is pointed out that it is possible to use radio frequency machine learning to build a classifier of radio frequency fingerprint for electronic facilities. Practical examples of radio frequency machine learning applications are given.

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Sun, Liting, Xiang Wang, Zhitao Huang, and Baoguo Li. "Radio Frequency Fingerprint Extraction based on Feature Inhomogeneity." IEEE Internet of Things Journal, 2022, 1. http://dx.doi.org/10.1109/jiot.2022.3154595.

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Yang, Yang, Aiqun Hu, Yuexiu Xing, Jiabao Yu, and Zhen Zhang. "A Data-independent Radio Frequency Fingerprint Extraction Scheme." IEEE Wireless Communications Letters, 2021, 1. http://dx.doi.org/10.1109/lwc.2021.3106396.

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Journal articles on the topic 'Radio Frequency Fingerprint'

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