## 6 Conclusions  
In this paper, a novel identification scheme based on radio frequency fingerprints is presented for DSRC intelligent vehicle networking devices adopting IEEE 802.11p protocol, in which the preamble field features of PPDU frames sent by the DSRC devices are extracted as device fingerprints, and the random forest algorithm and sequential detection method are used to realize the distinction and authentication of different devices. Compared with the existing V2X authentication technologies, the physical layer security technology based on RFF authentication utilizes the unique hardware characteristics of the devices, which can improve the performance against identity forgery without adding extra computation overhead.  
The identification method proposed in this article focuses on the impact of mobile multipath channels on RFFs. In the LOS and NLOS mobile experiment scenes and the simulated high-speed scenario up to 70 km/h, the recognition rate of eight DSRC modules of the same model can reach more than 99%. Thus, it can be seen that the RFF features extracted by this method can maintain good stability and location invariance with less influence by mobile channel, and this method has a high practical value in the field of authentication and identification of V2X devices. In future research, the number and model of devices to be identified can be further increased, and the experiment can be conducted in a more complex and changeable environment.  
Conceptualization, T.C., A.H. and Y.J.; Data Curation, T.C.; Formal Analysis, T.C., A.H. and Y.J.; Funding Acquisition, A.H. and Y.J.; Investigation, T.C.; Methodology, T.C., A.H. and Y.J.; Project Administration, A.H. and Y.J.; Resources, A.H. and Y.J.; Software, T.C.; Supervision, A.H. and Y.J.; Validation, T.C.; Visualization, T.C.; Writing--Original Draft, T.C.; Writing--Review and Editing, T.C. and A.H. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Module Number**} & \multicolumn{6}{c}{**Speeds (km/h)**} \\ \cline{2-9}  & **0** & **10** & **20** & **30** & **40** & **50** & **60** & **70** \\ \hline
1 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 99.90 \\
2 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\
3 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\
4 & 100 & 100 & 100 & 99.50 & 100 & 100 & 99.90 & 96.70 \\
5 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\
6 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\
7 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 \\
8 & 100 & 100 & 100 & 99.25 & 100 & 94.95 & 97.55 & 99.45 \\ Overall Average & 100 & 100 & 100 & 99.84 & 100 & 99.37 & 99.68 & 99.51 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Identification accuracy rate (%) of eight ESP8266 modules based on RFF features in simulated high mobility environment.  
**Funding:** This research was funded in part by Jiangsu Provincial Key Laboratory of Network and Information Security No. BM2003201, Jiangsu Key R & D Plan No. BE2019109, the National Natural Science Foundation of China under Grant 61601114, 61602113, 61801115, 61941115, 62001106, Natural Science Foundation of Jiangsu Province under Grant BK20160692, BK20200350, BK20200352, and the Purple Mountain Laboratories for Network and Communication Security.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.