A new dynamic clustering scheme for VANETs driven by deep reinforcement learning

Rongbai Zhang; Kezhi Wu

doi:10.1117/12.2675347

25 May 2023 A new dynamic clustering scheme for VANETs driven by deep reinforcement learning

Rongbai Zhang, Kezhi Wu

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 1263611 (2023) https://doi.org/10.1117/12.2675347
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

Vehicular nodes in Vehicular Ad Hoc Networks (VANETs) are featured with high speed, unpredictable sudden acceleration, and constantly changing positions. Due to the continuous mobility, the topology of VANETs becomes highly dynamic, which causes difficulties in effective data transmissions among V2V and V2I. The difficulties are originated from high communication overheads for exchanging and updating the topology information, transmission delay or interruption caused by high-speed movements. In order to boost up the performance of VANETs, we take the clustering as an efficient method to segment the moving vehicles in the traffic environment into different subgroups. In regards to the service of vehicle network, some tasks like video transmissions with long periods or intensive data interactions require a stable connection within the cluster, the longer lifetime is one of important factors for maintaining the dynamics as well as the cluster stability. To improve the life time of cluster, we develop the reinforcement learning (RL) agents into self-organized VANETs to form stable clusters and therefore increase the average lifetime, which can interact with the dynamic vehicle environment and output the optimal policy about cluster creation. A simulation is conducted to compare our RL-based scheme against other schemes with no RL factors. The results show that our method outperforms other dynamic schemes in the context of a high lifetime in comparison with the fixed policy. The average life time of our scheme has 6.5 % and 13.6 % improvement approximately in the statics of cluster change time and average vehicle’s speed.

Citation Download Citation

Rongbai Zhang and Kezhi Wu "A new dynamic clustering scheme for VANETs driven by deep reinforcement learning", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 1263611 (25 May 2023); https://doi.org/10.1117/12.2675347

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Head

Machine learning

Deep learning

Roads

Education and training

Detection and tracking algorithms

Intelligent sensors

Show All Keywords

Keywords/Phrases

Search In:

Publication Years