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Using Deep Reinforcement Learning to Automate Network Configurations for Internet of Vehicles

Published

Author(s)

Xing Liu, Cheng Qian, Wei Yu, David W. Griffith, Avi Gopstein, Nada T. Golmie

Abstract

In this paper, we address the issue of automating network configurations for dynamic network environments such as the Internet of Vehicles (IoV). Configuring network settings in IoV environments has proven to be a difficult task due to their dynamic and self-organizing nature. To address this issue, we propose a deep reinforcement learning-based approach to configure IoV network settings automatically. Specifically, we use a collection of neural networks to convert the observations of a communication environment (channel power gain, crosschannel power gain, etc.) into key features, which are then supplied to a deep Q neural network (DQN) as input for training. Afterwards, the DQN will select the optimal network configuration for vehicles in the IoV environment. In addition, our approach considers both centralized and distributed training strategies. The centralized training strategy conducts the DQN training process on a roadside server, while the distributed training strategy trains the DQN on vehicles locally. Through our designed IoV simulation platform, we evaluate the efficacy of our proposed approach, demonstrating that it can improve the quality of services (QoS) in the IoV environments, with respect to reliability, latency, and service satisfaction.
Citation
IEEE Transactions on Intelligent Transportation Systems

Keywords

Internet of Vehicles, Deep Reinforcement Learning

Citation

Liu, X. , Qian, C. , Yu, W. , Griffith, D. , Gopstein, A. and Golmie, N. (2023), Using Deep Reinforcement Learning to Automate Network Configurations for Internet of Vehicles, IEEE Transactions on Intelligent Transportation Systems, [online], https://doi.org/10.1109/TITS.2023.3308070, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935663 (Accessed May 22, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created September 4, 2023, Updated November 6, 2023