{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T18:41:14Z","timestamp":1772822474445,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2022,6,5]],"date-time":"2022-06-05T00:00:00Z","timestamp":1654387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ENSTA Bretagne of Brest"},{"name":"IBNM CyberIoT Chair of Excellence of the University of Brest"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In IoT networks, authentication of nodes is primordial and RF fingerprinting is one of the candidates as a non-cryptographic method. RF fingerprinting is a physical-layer security method consisting of authenticated wireless devices using their components\u2019 impairments. In this paper, we propose the RF eigenfingerprints method, inspired by face recognition works called eigenfaces. Our method automatically learns important features using singular value decomposition (SVD), selects important ones using Ljung\u2013Box test, and performs authentication based on a statistical model. We also propose simulation, real-world experiment, and FPGA implementation to highlight the performance of the method. Particularly, we propose a novel RF fingerprinting impairments model for simulation. The end of the paper is dedicated to a discussion about good properties of RF fingerprinting in IoT context, giving our method as an example. Indeed, RF eigenfingerprint has interesting properties such as good scalability, low complexity, and high explainability, making it a good candidate for implementation in IoT context.<\/jats:p>","DOI":"10.3390\/s22114291","type":"journal-article","created":{"date-parts":[[2022,6,5]],"date-time":"2022-06-05T10:47:11Z","timestamp":1654426031000},"page":"4291","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["RF eigenfingerprints, an Efficient RF Fingerprinting Method in IoT Context"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5739-7801","authenticated-orcid":false,"given":"Louis","family":"Morge-Rollet","sequence":"first","affiliation":[{"name":"ENSTA Bretagne, Lab-STICC, CNRS, UMR 6285, F-29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fr\u00e9d\u00e9ric","family":"Le Roy","sequence":"additional","affiliation":[{"name":"ENSTA Bretagne, Lab-STICC, CNRS, UMR 6285, F-29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Denis","family":"Le Jeune","sequence":"additional","affiliation":[{"name":"ENSTA Bretagne, Lab-STICC, CNRS, UMR 6285, F-29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Charles","family":"Canaff","sequence":"additional","affiliation":[{"name":"ENSTA Bretagne, Lab-STICC, CNRS, UMR 6285, F-29200 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3570-1061","authenticated-orcid":false,"given":"Roland","family":"Gautier","sequence":"additional","affiliation":[{"name":"Lab-STICC, Universit\u00e9 de Bretagne Occidentale, CEDEX 3, F-29238 Brest, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,6,5]]},"reference":[{"key":"ref_1","first-page":"6122","article-title":"Internet of Things-IOT: Definition, Characteristics, Architecture, Enabling Technologies, Application and Future Challenges","volume":"6","author":"Patel","year":"2016","journal-title":"Int. J. Eng. Sci. Comput."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Shah, S.H., and Yaqoob, I. (2016, January 21\u201324). A survey: Internet of Things (IOT) technologies, applications and challenges. Proceedings of the 2016 IEEE Smart Energy Grid Engineering (SEGE), Oshawa, ON, Canada.","DOI":"10.1109\/SEGE.2016.7589556"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1109\/TCCN.2019.2949308","article-title":"No Radio Left Behind: Radio Fingerprinting Through Deep Learning of Physical-Layer Hardware Impairments","volume":"6","author":"Sankhe","year":"2020","journal-title":"IEEE Trans. Cogn. Commun. Netw."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1109\/MWC.2010.5601959","article-title":"Non-cryptographic authentication and identification in wireless networks [Security and Privacy in Emerging Wireless Networks]","volume":"17","author":"Zeng","year":"2010","journal-title":"IEEE Wirel. Commun."},{"key":"ref_5","unstructured":"Morge-Rollet, L., Le Roy, F., Le Jeune, D., and Gautier, R. (2020). Siamese Network on I\/Q Signals for RF fingerprinting. Actes de la Conf\u00e9rence CAID 2020, Hindustan Aeronautics Limited."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mattei, E., Dalton, C., Draganov, A., Marin, B., Tinston, M., Harrison, G., Smarrelli, B., and Harlacher, M. (2019, January 11\u201314). Feature Learning for Enhanced Security in the Internet of Things. Proceedings of the 2019 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Ottawa, ON, Canada.","DOI":"10.1109\/GlobalSIP45357.2019.8969222"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1364\/JOSAA.4.000519","article-title":"Low-dimensional procedure for the characterization of human faces","volume":"4","author":"Sirovich","year":"1987","journal-title":"J. Opt. Soc. Am. A Opt. Image Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/34.41390","article-title":"Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces","volume":"12","author":"Kirby","year":"1990","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","unstructured":"Aur\u00e9lien, G. (2017). Hands-On Machine Learning with Scikit-Learn, Keras and TensorFlow, O\u2019Reilly Media, Inc."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Brunton, S.L., and Kutz, J.N. (2019). Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control, Cambridge University Press.","DOI":"10.1017\/9781108380690"},{"key":"ref_11","unstructured":"Turk, M.A., and Pentl, A.P. (1991, January 3\u20136). Face recognition using eigenfaces. Proceedings of the 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Maui, HI, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1162\/jocn.1991.3.1.71","article-title":"Eigenfaces for Recognition","volume":"3","author":"Turk","year":"1991","journal-title":"J. Cogn. Neurosci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1109\/34.927464","article-title":"From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose","volume":"23","author":"Georghiades","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1109\/TPAMI.2005.92","article-title":"Acquiring linear subspaces for face recognition under variable lighting","volume":"27","author":"Lee","year":"2005","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, S., Qin, H., Liang, X., and Gulliver, T.A. (2019). An Improved Unauthorized Unmanned Aerial Vehicle Detection Algorithm Using Radiofrequency-Based Statistical Fingerprint Analysis. Sensors, 19.","DOI":"10.3390\/s19020274"},{"key":"ref_16","first-page":"171","article-title":"Device fingerprinting using deep convolutional neural networks","volume":"28","author":"Aneja","year":"2022","journal-title":"Int. Commun. Netw. Distrib. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1109\/JRFID.2020.2968369","article-title":"A Review of Radio Frequency Fingerprinting Techniques","volume":"4","author":"Soltanieh","year":"2020","journal-title":"IEEE J. Radio Freq. Identif."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Robyns, P., Marin, E., Lamotte, W., Quax, P., Singel\u00e9e, D., and Preneel, B. (2017, January 18). Physical-layer fingerprinting of LoRa devices using supervised and zero-shot learning. Proceedings of the 10th ACM Conference on Security and Privacy in Wireless and Mobile Networks, Boston, MA, USA.","DOI":"10.1145\/3098243.3098267"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Guo, X., Zhang, Z., and Chang, J. (2019, January 29). Survey of Mobile Device Authentication Methods Based on RF fingerprint. Proceedings of the IEEE INFOCOM 2019\u2014IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), Paris, France.","DOI":"10.1109\/INFOCOMWKSHPS47286.2019.9093755"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Brik, V., Banerjee, S., Gruteser, M., and Oh, S. (2008, January 14\u201319). Wireless device identification with radiometric signatures. Proceedings of the 14th ACM International Conference on Mobile Computing and Networking, San Francisco, CA, USA.","DOI":"10.1145\/1409944.1409959"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1109\/MCOM.2018.1800153","article-title":"Deep Learning Convolutional Neural Networks for Radio Identification","volume":"56","author":"Riyaz","year":"2018","journal-title":"IEEE Commun. Mag."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Tian, Q., Lin, Y., Guo, X., Wang, J., AlFarraj, O., and Tolba, A. (2020). An Identity Authentication Method of a MIoT Device Based on Radio Frequency (RF) Fingerprint Technology. Sensors, 20.","DOI":"10.3390\/s20041213"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Mohamed, I., Dalveren, Y., Catak, F.O., and Kara, A. (2022). On the Performance of Energy Criterion Method in Wi-Fi Transient Signal Detection. Electronics, 11.","DOI":"10.3390\/electronics11020269"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Aghnaiya, A., Dalveren, Y., and Kara, A. (2020). On the Performance of Variational Mode Decomposition-Based Radio Frequency Fingerprinting of Bluetooth Devices. Sensors, 20.","DOI":"10.3390\/s20061704"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"472","DOI":"10.1016\/j.procs.2017.03.092","article-title":"Yuanling Huang and Jian Chen. Radio Frequency Fingerprint Extraction of Radio Emitter Based on I\/Q Imbalance","volume":"107","author":"Huang","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_26","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2018). Yoshua Bengin and Aaron Courville. Deep Learning, The MIT Press."},{"key":"ref_27","unstructured":"John, D. (2022, April 15). Radio Frequency Machine Learning Systems (RFMLS). Available online: https:\/\/www.darpa.mil\/program\/radio-frequency-machine-learning-systems."},{"key":"ref_28","unstructured":"(2022, April 15). The Radio Frequency Spectrum + Machine Learning = A New Wave in Radio Technology. Available online: https:\/\/www.darpa.mil\/news-events\/2017-08-11a."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"O\u2019Shea, T.J., Corgan, J., and Clancy, T.C. (2016, January 2\u20135). Convolutional Radio Modulation Recognition Networks. Proceedings of the International Conference on Engineering Applications of Neural Networks, Aberdeen, UK.","DOI":"10.1007\/978-3-319-44188-7_16"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"103291","DOI":"10.1109\/ACCESS.2019.2929311","article-title":"Feature Reduction Method for Cognition and Classification of IoT Devices Based on Artificial Intelligence","volume":"7","author":"Chen","year":"2019","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1109\/JIOT.2018.2838071","article-title":"Design of a Hybrid RF fingerprint Extraction and Device Classification Scheme","volume":"6","author":"Peng","year":"2019","journal-title":"IEEE Int. Things J."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, S., Wen, H., Wu, J., Xu, A., Jiang, Y., Song, H., and Chen, Y. (2019). Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication. Sensors, 19.","DOI":"10.3390\/s19163610"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Gutierrez del Arroyo, J.A., Borghetti, B.J., and Temple, M.A. (2022). Considerations for Radio Frequency Fingerprinting across Multiple Frequency Channels. Sensors, 22.","DOI":"10.3390\/s22062111"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101250","DOI":"10.1016\/j.phycom.2020.101250","article-title":"Radio frequency fingerprinting identification for Zigbee via lightweight CNN","volume":"44","author":"Qing","year":"2021","journal-title":"Phys. Commun."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/IOTM.0001.1900065","article-title":"Deep Learning for RF Fingerprinting: A Massive Experimental Study","volume":"3","author":"Jian","year":"2020","journal-title":"IEEE Internet Things Mag."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Brockwell, P.J., and Davis, R.A. (1996). Introduction to Time Series and Forecasting, Springer.","DOI":"10.1007\/978-1-4757-2526-1"},{"key":"ref_37","unstructured":"Stoica, P., and Moses, R.L. (2005). Spectral Analysis of Signals, Pearson Prentice Hall."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"73","DOI":"10.32614\/RJ-2015-006","article-title":"The Complex Multivariate Gaussian Distribution","volume":"7","author":"Hankin","year":"2015","journal-title":"R J."},{"key":"ref_39","first-page":"152","article-title":"Statistical analysis based on a certain multivariate complex Gaussian distribution","volume":"34","author":"Goodman","year":"1963","journal-title":"Proc. IEEE"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nguyen, N.T., Zheng, G., Han, Z., and Zheng, R. (2011, January 11\u201315). Device fingerprinting to enhance wireless security using nonparametric Bayesian method. Proceedings of the 2011 Proceedings IEEE INFOCOM (2011), Shanghai, China.","DOI":"10.1109\/INFCOM.2011.5934926"},{"key":"ref_41","unstructured":"Scott, I. (2022, April 15). Analogue IQ Error Correction For Transmitters\u2014Off Line Method. Available online: http:\/\/vaedrah.angelfire.com."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1109\/TMTT.2005.860500","article-title":"A comparative analysis of behavioral models for RF power amplifiers","volume":"54","author":"Isaksson","year":"2006","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_43","unstructured":"Ozturk, E., Erden, F., and Guvenc, I. (2020). RF-Based Low-SNR Classification of UAVs Using Convolutional Neural Networks. arXiv."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Sharif, M.U., Shahid, R., Gaj, K., and Rogawski, M. (September, January 29). Hardware-software codesign of RSA for optimal performance vs. flexibility trade-off. Proceedings of the 2016 26th International Conference on Field Programmable Logic and Applications (FPL), Lausanne, Switzerland.","DOI":"10.1109\/FPL.2016.7577368"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"282","DOI":"10.1109\/COMST.2020.3042188","article-title":"A Survey of Physical-Layer Authentication in Wireless Communications","volume":"23","author":"Xie","year":"2021","journal-title":"IEEE Commun. Surv. Tutorials"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1743","DOI":"10.1109\/TSE.2020.3034721","article-title":"Towards Security Threats of Deep Learning Systems: A Survey","volume":"48","author":"He","year":"2021","journal-title":"IEEE Trans. Softw. Eng."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"West, N.E., and O\u2019Shea, T. (2017, January 6). Deep architectures for modulation recognition. Proceedings of the 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), Baltimore, MD, USA.","DOI":"10.1109\/DySPAN.2017.7920754"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Kuzdeba, S., Carmack, J., and Robinson, J. (November, January 31). RF Fingerprinting with Dilated Causal Convolutions\u2013An Inherently Explainable Architecture. Proceedings of the 2021 55th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, USA.","DOI":"10.1109\/IEEECONF53345.2021.9723341"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Tse, D., and Viswanath, P. (2004). Fundamentals of Wireless Communication, Cambridge University Press.","DOI":"10.1017\/CBO9780511807213"},{"key":"ref_50","unstructured":"Rice, M.D. (2008). Digital Communications: A Discrete-Time Approach, Pearson Education India."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/11\/4291\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T23:24:40Z","timestamp":1760138680000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/11\/4291"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,5]]},"references-count":50,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2022,6]]}},"alternative-id":["s22114291"],"URL":"https:\/\/doi.org\/10.3390\/s22114291","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,5]]}}}