The Individual Identification Method of Wireless Device Based on A Robust Dimensionality Reduction Model of Hybrid Feature Information

With the advent of Internet of things, the number of mobile, and embedded, 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 fingerprint features through integral envelope and Hilbert transform theory based PCA analysis algorithm. Integral envelope theory and Hilbert transform 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 the effectiveness of the proposed algorithm to differentiat between 10 numbers of wireless device with the accuracy in excess of 99%. The approach itself is general and will work with any wireless devices’ recognition.