Secrecy Performance Intelligent Prediction for Mobile Vehicular Networks: An DI-CNN Approach

L. Xu, H. Tang, H. Li, X. Li, T.A. Gulliver, Khoa N. Le

Research output: Contribution to journalArticlepeer-review

Abstract

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.
Original languageEnglish
Pages (from-to)7363-7373
Number of pages11
JournalIEEE Transactions on Intelligent Transportation Systems
Volume25
Issue number7
DOIs
Publication statusPublished - 2024

Bibliographical note

Publisher Copyright:
© 2000-2011 IEEE.

Keywords

  • secrecy capacity
  • DI-CNN
  • Mobile IoV network
  • physical layer security

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