Cargando…

Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication †

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-bas...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Songlin, Wen, Hong, Wu, Jinsong, Xu, Aidong, Jiang, Yixin, Song, Huanhuan, Chen, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6720791/
https://www.ncbi.nlm.nih.gov/pubmed/31430988
http://dx.doi.org/10.3390/s19163610
_version_ 1783448206002094080
author Chen, Songlin
Wen, Hong
Wu, Jinsong
Xu, Aidong
Jiang, Yixin
Song, Huanhuan
Chen, Yi
author_facet Chen, Songlin
Wen, Hong
Wu, Jinsong
Xu, Aidong
Jiang, Yixin
Song, Huanhuan
Chen, Yi
author_sort Chen, Songlin
collection PubMed
description 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.
format Online
Article
Text
id pubmed-6720791
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-67207912019-09-10 Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication † Chen, Songlin Wen, Hong Wu, Jinsong Xu, Aidong Jiang, Yixin Song, Huanhuan Chen, Yi Sensors (Basel) Article 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. MDPI 2019-08-19 /pmc/articles/PMC6720791/ /pubmed/31430988 http://dx.doi.org/10.3390/s19163610 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Chen, Songlin
Wen, Hong
Wu, Jinsong
Xu, Aidong
Jiang, Yixin
Song, Huanhuan
Chen, Yi
Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication †
title Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication †
title_full Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication †
title_fullStr Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication †
title_full_unstemmed Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication †
title_short Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication †
title_sort radio frequency fingerprint-based intelligent mobile edge computing for internet of things authentication †
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6720791/
https://www.ncbi.nlm.nih.gov/pubmed/31430988
http://dx.doi.org/10.3390/s19163610
work_keys_str_mv AT chensonglin radiofrequencyfingerprintbasedintelligentmobileedgecomputingforinternetofthingsauthentication
AT wenhong radiofrequencyfingerprintbasedintelligentmobileedgecomputingforinternetofthingsauthentication
AT wujinsong radiofrequencyfingerprintbasedintelligentmobileedgecomputingforinternetofthingsauthentication
AT xuaidong radiofrequencyfingerprintbasedintelligentmobileedgecomputingforinternetofthingsauthentication
AT jiangyixin radiofrequencyfingerprintbasedintelligentmobileedgecomputingforinternetofthingsauthentication
AT songhuanhuan radiofrequencyfingerprintbasedintelligentmobileedgecomputingforinternetofthingsauthentication
AT chenyi radiofrequencyfingerprintbasedintelligentmobileedgecomputingforinternetofthingsauthentication