IEICE TRANSACTIONS on Communications
A Hybrid Routing Algorithm for V2V Communication in VANETs Based on Blocked Q-Learning
Summary :
It is of great significance to design a stable and reliable routing protocol for Vehicular Ad Hoc Networks (VANETs) that adopt Vehicle to Vehicle (V2V) communications in the face of frequent network topology changes. In this paper, we propose a hybrid routing algorithm, RCRIQ, based on improved Q-learning. For an established cluster structure, the cluster head is used to select the gateway vehicle according to the gateway utility function to expand the communication range of the cluster further. During the link construction stage, an improved Q-learning algorithm is adopted. The corresponding neighbor vehicle is chosen according to the maximum Q value in the neighbor list. The heuristic algorithm selects the next-hop by the maximum heuristic function value when selecting the next-hop neighbor node. The above two strategies are comprehensively evaluated to determine the next hop. This way ensures the optimal selection of the next hop in terms of reachability and other communication parameters. Simulation experiments show that the algorithm proposed in this article has better performance in terms of routing stability, throughput, and communication delay in the urban traffic scene.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E106-B No.1 pp.1-17
- Publication Date
- 2023/01/01
- Publicized
- 2022/05/31
- Online ISSN
- 1745-1345
- DOI
- 10.1587/transcom.2021EBP3210
- Type of Manuscript
- PAPER
- Category
- Network
Authors
Xiang BI
Hefei University of Technology,Wuhu Token Sciences Co., Ltd.
Huang HUANG
Hefei University of Technology
Benhong ZHANG
Hefei University of Technology,Ministry of Education
Xing WEI
Hefei University of Technology
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Xiang BI, Huang HUANG, Benhong ZHANG, Xing WEI, "A Hybrid Routing Algorithm for V2V Communication in VANETs Based on Blocked Q-Learning" in IEICE TRANSACTIONS on Communications,
vol. E106-B, no. 1, pp. 1-17, January 2023, doi: 10.1587/transcom.2021EBP3210.
Abstract: It is of great significance to design a stable and reliable routing protocol for Vehicular Ad Hoc Networks (VANETs) that adopt Vehicle to Vehicle (V2V) communications in the face of frequent network topology changes. In this paper, we propose a hybrid routing algorithm, RCRIQ, based on improved Q-learning. For an established cluster structure, the cluster head is used to select the gateway vehicle according to the gateway utility function to expand the communication range of the cluster further. During the link construction stage, an improved Q-learning algorithm is adopted. The corresponding neighbor vehicle is chosen according to the maximum Q value in the neighbor list. The heuristic algorithm selects the next-hop by the maximum heuristic function value when selecting the next-hop neighbor node. The above two strategies are comprehensively evaluated to determine the next hop. This way ensures the optimal selection of the next hop in terms of reachability and other communication parameters. Simulation experiments show that the algorithm proposed in this article has better performance in terms of routing stability, throughput, and communication delay in the urban traffic scene.
URL: https://global.ieice.org/en_transactions/communications/10.1587/transcom.2021EBP3210/_p
Copy
@ARTICLE{e106-b_1_1,
author={Xiang BI, Huang HUANG, Benhong ZHANG, Xing WEI, },
journal={IEICE TRANSACTIONS on Communications},
title={A Hybrid Routing Algorithm for V2V Communication in VANETs Based on Blocked Q-Learning},
year={2023},
volume={E106-B},
number={1},
pages={1-17},
abstract={It is of great significance to design a stable and reliable routing protocol for Vehicular Ad Hoc Networks (VANETs) that adopt Vehicle to Vehicle (V2V) communications in the face of frequent network topology changes. In this paper, we propose a hybrid routing algorithm, RCRIQ, based on improved Q-learning. For an established cluster structure, the cluster head is used to select the gateway vehicle according to the gateway utility function to expand the communication range of the cluster further. During the link construction stage, an improved Q-learning algorithm is adopted. The corresponding neighbor vehicle is chosen according to the maximum Q value in the neighbor list. The heuristic algorithm selects the next-hop by the maximum heuristic function value when selecting the next-hop neighbor node. The above two strategies are comprehensively evaluated to determine the next hop. This way ensures the optimal selection of the next hop in terms of reachability and other communication parameters. Simulation experiments show that the algorithm proposed in this article has better performance in terms of routing stability, throughput, and communication delay in the urban traffic scene.},
keywords={},
doi={10.1587/transcom.2021EBP3210},
ISSN={1745-1345},
month={January},}
Copy
TY - JOUR
TI - A Hybrid Routing Algorithm for V2V Communication in VANETs Based on Blocked Q-Learning
T2 - IEICE TRANSACTIONS on Communications
SP - 1
EP - 17
AU - Xiang BI
AU - Huang HUANG
AU - Benhong ZHANG
AU - Xing WEI
PY - 2023
DO - 10.1587/transcom.2021EBP3210
JO - IEICE TRANSACTIONS on Communications
SN - 1745-1345
VL - E106-B
IS - 1
JA - IEICE TRANSACTIONS on Communications
Y1 - January 2023
AB - It is of great significance to design a stable and reliable routing protocol for Vehicular Ad Hoc Networks (VANETs) that adopt Vehicle to Vehicle (V2V) communications in the face of frequent network topology changes. In this paper, we propose a hybrid routing algorithm, RCRIQ, based on improved Q-learning. For an established cluster structure, the cluster head is used to select the gateway vehicle according to the gateway utility function to expand the communication range of the cluster further. During the link construction stage, an improved Q-learning algorithm is adopted. The corresponding neighbor vehicle is chosen according to the maximum Q value in the neighbor list. The heuristic algorithm selects the next-hop by the maximum heuristic function value when selecting the next-hop neighbor node. The above two strategies are comprehensively evaluated to determine the next hop. This way ensures the optimal selection of the next hop in terms of reachability and other communication parameters. Simulation experiments show that the algorithm proposed in this article has better performance in terms of routing stability, throughput, and communication delay in the urban traffic scene.
ER -
English
