TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
A Diversified Message Type Forwarding Strategy Based on Reinforcement Learning in VANET |
| 영문제목(English Title) |
A Diversified Message Type Forwarding Strategy Based on Reinforcement Learning in VANET |
| 저자(Author) |
Guoai Xu
Boya Liu
Guosheng Xu
Peiliang Zuo
|
| 원문수록처(Citation) |
VOL 16 NO. 09 PP. 3104 ~ 3123 (2022. 09) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
The development of Vehicular Ad hoc Network (VANET) has greatly improved the efficiency and safety of social transportation, and the routing strategy for VANET has also received high attention from both academia and industry. However, studies on dynamic matching of routing policies with the message types of VANET are in short supply, which affects the operational efficiency and security of VANET to a certain extent. This paper studies the message types in VANET and fully considers the urgency and reliability requirements of message forwarding under various types. Based on the diversified types of messages to be transmitted, and taking the diversified message forwarding strategies suitable for VANET scenarios as behavioral candidates, an adaptive routing method for the VANET message types based on reinforcement learning (RL) is proposed. The key parameters of the method, such as state, action and reward, are reasonably designed. Simulation and analysis show that the proposed method could converge quickly, and the comprehensive performance of the proposed method is obviously better than the comparison methods in terms of timeliness and reliability. |
| 키워드(Keyword) |
Routing strategy
Message Forwarding Strategy
Reinforcement Learning
VANET
Black nodes
|
| 파일첨부 |
PDF 다운로드
|
