Abstract
With the advent of the Internet of Things, the number of mobile, embedded, and wearable devices are on the rising nowadays, which make us increasingly faced with the limitations of traditional network security control. Hence, accurately identifying different wireless devices through Hybrid information processing method for the Internet of things becomes very important today. To this problem, we design, implement, and evaluate a robust algorithm to identify the wireless device with fingerprints features through integral envelope and Hilbert transform theory based PCA analysis algorithm. Integral envelope theory was used respectively to process the signals first, then the principal component features can be extracted by PCA analysis algorithm. At last, gray relation classifier was used to identify the signals. We experimentally demonstrate effectiveness of the proposed algorithm ixin differentiating between 500 numbers of wireless device with the accuracy in excess of 99%. The approach itself is general and will work with any wireless devices’ recognition.
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The research of the paper is supported by the National Natural Science Foundation of China (No. 61603239) and (No. 61601281).
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Han, H., Li, J., Chen, X. (2018). A Novel Individual Radio Identification Algorithm Based on Multi-dimensional Features and Gray Relation Theory. In: Sun, G., Liu, S. (eds) Advanced Hybrid Information Processing. ADHIP 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 219. Springer, Cham. https://doi.org/10.1007/978-3-319-73317-3_16
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DOI: https://doi.org/10.1007/978-3-319-73317-3_16
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