Journal
COMPUTER NETWORKS
Volume 238, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.comnet.2023.110115
Keywords
Radio frequency fingerprint; Smartphone identification; Carrier frequency offset; Crystal oscillator temperature
Ask authors/readers for more resources
This study proposes a radio frequency fingerprint identification solution based on crystal oscillator temperature adjustment, which enhances the differences between Wi-Fi device fingerprints and mitigates collision. Experimental results demonstrate the effectiveness of the system in identifying smartphones under different scenarios.
Recently, radio frequency fingerprints (RFFs) have been widely applied for smartphone identification, since RFFs are distinguishable and hard to imitate. Compared with other types of RFFs, carrier frequency offset (CFO) in Wi-Fi signals is more robust and practical. However, when many smartphones need to be identified, the probability of CFO collision is high due to the weak distinguishability of CFO, and thus the primitive CFO-based identification solution will perform poorly. Fortunately, we find that the CFO varies with crystal oscillator temperature. Inspired by the phenomenon, we can actively adjust the crystal oscillator temperature to increase the difference between Wi-Fi device fingerprints for CFO collision mitigation. Firstly, we propose a non-intrusive temperature sensing and adjustment solution, which can obtain crystal oscillator temperature accurately and actively adjust its temperature to specified values without any additional hardware. Then, we investigate the temperature selection problem which aims to maximize overall differences among all smartphones, and propose the corresponding algorithm that combines greedy strategy and simulated annealing to assign a proper temperature value to each smartphone for identification. Finally, we implement the TEA-RFFI system and conduct several sets of experiments under the cases of different positions, time periods and scenarios. Experimental results demonstrate that TEA-RFFI can effectively identify 20 smartphones with over 90% precision, recall and F1-score. Even when the smartphones are moving, our proposed system still can identify them with over 89% precision, recall and F1-score.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available