{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T14:29:48Z","timestamp":1773844188754,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T00:00:00Z","timestamp":1566172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2018YFB0904900\uff0c2018YFB0904905"],"award-info":[{"award-number":["2018YFB0904900\uff0c2018YFB0904905"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s19163610","type":"journal-article","created":{"date-parts":[[2019,8,19]],"date-time":"2019-08-19T11:22:38Z","timestamp":1566213758000},"page":"3610","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Radio Frequency Fingerprint-Based Intelligent Mobile Edge Computing for Internet of Things Authentication"],"prefix":"10.3390","volume":"19","author":[{"given":"Songlin","family":"Chen","sequence":"first","affiliation":[{"name":"National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8645-2264","authenticated-orcid":false,"given":"Hong","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4720-5946","authenticated-orcid":false,"given":"Jinsong","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China"},{"name":"Department of Electrical Engineering, Universidad de Chile, Av Tupper 2007, Santiago 8370451, Chile"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aidong","family":"Xu","sequence":"additional","affiliation":[{"name":"EPRI, China Southern Power Grid Co. Ltd., Guangzhou 510080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yixin","family":"Jiang","sequence":"additional","affiliation":[{"name":"EPRI, China Southern Power Grid Co. Ltd., Guangzhou 510080, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huanhuan","family":"Song","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Communications, University of Electronic Science and Technology of China, Chengdu 611731, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"854","DOI":"10.1109\/JIOT.2016.2584538","article-title":"Fog and IoT: An overview of research opportunities","volume":"3","author":"Chiang","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2322","DOI":"10.1109\/COMST.2017.2745201","article-title":"A survey on mobile edge computing: The communication perspective","volume":"19","author":"Mao","year":"2017","journal-title":"IEEE Commun. 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