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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

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Deep Reinforcement Learning-Based Intelligent Security Forwarding Strategy for VANET.

Boya Liu^1,2,3, Guoai Xu^1,3, Guosheng Xu^1,3

¹School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China.

Sensors (Basel, Switzerland)

|February 11, 2023

Summary

This summary is machine-generated.

This study introduces a secure intelligent message forwarding strategy for vehicular ad hoc networks (VANETs) using deep reinforcement learning (DRL). The method enhances safety, efficiency, and reduces network delay for intelligent transportation systems.

Keywords:

DRL VANET intelligent malicious node message type

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Area of Science:

Intelligent Transportation Systems
Computer Networks
Artificial Intelligence

Background:

Vehicular ad hoc networks (VANETs) are crucial for intelligent transportation but face challenges with diverse messages, complex security, and dynamic topology.
Ensuring secure, efficient, and reliable message services in VANETs remains a significant challenge.
Existing routing strategies struggle to adapt to the dynamic nature and varied requirements of VANETs.

Purpose of the Study:

To propose a secure and intelligent message forwarding strategy for VANETs.
To enhance routing flexibility for multiple message types in dynamic network environments.
To improve the overall performance and security of VANET message services.

Main Methods:

A secure intelligent message forwarding strategy based on deep reinforcement learning (DRL) is proposed.
Deep Q-Networks (DQN) are utilized for model training.
The state space incorporates candidate-destination node distance, node security attributes, and message type.

Main Results:

The proposed strategy demonstrates fast convergence and strong generalization ability.
Simulation results show high transmission security and reduced network delay.
The strategy effectively adapts routing schemes to complex network states.

Conclusions:

The developed DRL-based strategy offers a flexible and secure solution for VANET message forwarding.
It addresses the challenges of diverse message types and dynamic network conditions.
The strategy contributes to safer, more efficient, and reliable intelligent transportation services.