Research on the Internet of Things Device Recognition Based on RF-Fingerprinting
Internet of Things (IoT) technology provides a large-scale network for information exchange and communication with big data. Because of the openness of IoT devices in the process of signal transmission, the recognition and access of different IoT devices are directly related to the wide application...
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doaj-493eef385cd44fcba45ff7f061a8385c2021-03-29T22:16:51ZengIEEEIEEE Access2169-35362019-01-017374263743110.1109/ACCESS.2019.29046578672959Research on the Internet of Things Device Recognition Based on RF-FingerprintingYa Tu0Zhen Zhang1Yibing Li2Chao Wang3Yihan Xiao4https://orcid.org/0000-0001-8785-1905College of Information and Communication Engineering, Harbin Engineering University, Harbin, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin, ChinaInternet of Things (IoT) technology provides a large-scale network for information exchange and communication with big data. Because of the openness of IoT devices in the process of signal transmission, the recognition and access of different IoT devices are directly related to the wide application of its system. The radio frequency fingerprinting (RFF) is a unique characteristic closely related to the hardware of IoT devices themselves, which is difficultly tampered. In this paper, four kinds of RF fingerprint feature extraction algorithms based on statistical features are studied. Robust principle component analysis (RPCA) is used for the dimensionality reduction and the support vector machines (SVM) is used for classification. Through theoretical modeling and experimental verification, the reliability and distinguishability of RFFs are extracted and evaluated, and the classification results are displayed in the real IoT equipment environment.https://ieeexplore.ieee.org/document/8672959/Internet of Thingsradio frequency identificationrobust principle component analysissupport vector machines |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Ya Tu Zhen Zhang Yibing Li Chao Wang Yihan Xiao |
spellingShingle |
Ya Tu Zhen Zhang Yibing Li Chao Wang Yihan Xiao Research on the Internet of Things Device Recognition Based on RF-Fingerprinting IEEE Access Internet of Things radio frequency identification robust principle component analysis support vector machines |
author_facet |
Ya Tu Zhen Zhang Yibing Li Chao Wang Yihan Xiao |
author_sort |
Ya Tu |
title |
Research on the Internet of Things Device Recognition Based on RF-Fingerprinting |
title_short |
Research on the Internet of Things Device Recognition Based on RF-Fingerprinting |
title_full |
Research on the Internet of Things Device Recognition Based on RF-Fingerprinting |
title_fullStr |
Research on the Internet of Things Device Recognition Based on RF-Fingerprinting |
title_full_unstemmed |
Research on the Internet of Things Device Recognition Based on RF-Fingerprinting |
title_sort |
research on the internet of things device recognition based on rf-fingerprinting |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Internet of Things (IoT) technology provides a large-scale network for information exchange and communication with big data. Because of the openness of IoT devices in the process of signal transmission, the recognition and access of different IoT devices are directly related to the wide application of its system. The radio frequency fingerprinting (RFF) is a unique characteristic closely related to the hardware of IoT devices themselves, which is difficultly tampered. In this paper, four kinds of RF fingerprint feature extraction algorithms based on statistical features are studied. Robust principle component analysis (RPCA) is used for the dimensionality reduction and the support vector machines (SVM) is used for classification. Through theoretical modeling and experimental verification, the reliability and distinguishability of RFFs are extracted and evaluated, and the classification results are displayed in the real IoT equipment environment. |
topic |
Internet of Things radio frequency identification robust principle component analysis support vector machines |
url |
https://ieeexplore.ieee.org/document/8672959/ |
work_keys_str_mv |
AT yatu researchontheinternetofthingsdevicerecognitionbasedonrffingerprinting AT zhenzhang researchontheinternetofthingsdevicerecognitionbasedonrffingerprinting AT yibingli researchontheinternetofthingsdevicerecognitionbasedonrffingerprinting AT chaowang researchontheinternetofthingsdevicerecognitionbasedonrffingerprinting AT yihanxiao researchontheinternetofthingsdevicerecognitionbasedonrffingerprinting |
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1724191943900004352 |