TY - JOUR
T1 - Secrecy Performance Intelligent Prediction for Mobile Vehicular Networks
T2 - An DI-CNN Approach
AU - Xu, L.
AU - Tang, H.
AU - Li, H.
AU - Li, X.
AU - Gulliver, T.A.
AU - Le, Khoa N.
N1 - Publisher Copyright:
© 2000-2011 IEEE.
PY - 2024
Y1 - 2024
N2 - The rapid expansion of Internet of Vehicles (IoV) networks has facilitated high throughput and reliable vehicular communications. Mobile vehicular networks face the challenges: diversification of network equipment, user mobility, and the broadcast nature of wireless channels, so physical layer security modeling of IoV communication systems has become important. The complexity of wireless communication channels makes real-time prediction of secrecy performance challenging. This paper presents an analysis of secrecy performance for mobile vehicular networks. To ensure data secure transmission, we have employed the decode-and-forward (DF) relaying scheme. The signal-to-noise ratio (SNR) of the effective end-to-end link is employed to obtain the mathematical expression results, which can evaluate the secrecy performance. The theoretical secrecy performance is confirmed via simulation. Then, we design a dense-inception convolution neural network (DI-CNN) model, and propose a DI-CNN-based intelligent prediction algorithm.Transformer, ShuffleNetV2, RegNet and YOLOv5 methods are employed to analyze the performance of DI-CNN algorithm. It is shown that the DI-CNN approach has a prediction accuracy that is 48.8% better than Transformer.
AB - The rapid expansion of Internet of Vehicles (IoV) networks has facilitated high throughput and reliable vehicular communications. Mobile vehicular networks face the challenges: diversification of network equipment, user mobility, and the broadcast nature of wireless channels, so physical layer security modeling of IoV communication systems has become important. The complexity of wireless communication channels makes real-time prediction of secrecy performance challenging. This paper presents an analysis of secrecy performance for mobile vehicular networks. To ensure data secure transmission, we have employed the decode-and-forward (DF) relaying scheme. The signal-to-noise ratio (SNR) of the effective end-to-end link is employed to obtain the mathematical expression results, which can evaluate the secrecy performance. The theoretical secrecy performance is confirmed via simulation. Then, we design a dense-inception convolution neural network (DI-CNN) model, and propose a DI-CNN-based intelligent prediction algorithm.Transformer, ShuffleNetV2, RegNet and YOLOv5 methods are employed to analyze the performance of DI-CNN algorithm. It is shown that the DI-CNN approach has a prediction accuracy that is 48.8% better than Transformer.
KW - secrecy capacity
KW - DI-CNN
KW - Mobile IoV network
KW - physical layer security
UR - https://hdl.handle.net/1959.7/uws:75878
UR - http://www.scopus.com/inward/record.url?scp=85187271499&partnerID=8YFLogxK
U2 - 10.1109/TITS.2024.3352668
DO - 10.1109/TITS.2024.3352668
M3 - Article
SN - 1558-0016
SN - 1524-9050
VL - 25
SP - 7363
EP - 7373
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 7
ER -