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

Clicks: 236
ID: 39235
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #1,069 of 1,694 articles by views in sensors

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 1,694 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
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.
Reference Key
chen2019radiosensors Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Chen, Songlin;Wen, Hong;Wu, Jinsong;Xu, Aidong;Jiang, Yixin;Song, Huanhuan;Chen, Yi;
Journal sensors
Year 2019
DOI
E3610
URL
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.