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ARSL-V: A risk-aware relay selection scheme using reinforcement learning in VANETs

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Abstract

In high-speed and dynamic Vehicular Ad-hoc Networks (VANETs), cooperative transmission mechanism is a promising scheme to ensure the sustainable transmission of data. However, due to the possible malicious behavior of vehicles and the dynamic network topology of VANETs, not all vehicles are trustworthy to become relays and perform the cooperative transmission task reliably. Therefore, how to ensure the security and reliability of the selected vehicles is still an urgent problem to be solved. In this paper, we propose a risk-aware relay selection scheme (ARSL-V) using reinforcement learning in VANETs. Specifically, we design a risk assessment mechanism based on multiple parameters to dynamically assess the potential risk of relay vehicles by considering the reputation variability, abnormal behavior, and environmental impact of vehicles. Also, we model the relay selection problem as an improved Kuhn-Munkres algorithm based on the risk assessment to realize relay selection in multi-relay and multi-target vehicle scenarios. Besides, we use a reinforcement learning algorithm combined with feedback data to achieve dynamic adjustment of the parameter weights. Simulation results show that compared with the existing schemes, ARSL-V can improve the detection rate of malicious behavior and cooperative transmission success rate by about 25% and 6%, respectively.

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Funding

This research is supported by Zhejiang Electronic Information Products Inspection and Research Institute(Key Laboratory of Information Security of Zhejiang Province) under Grant No. KF202303. and Natural Science Foundation of Zhejiang Province under Grant No.LZ22F030004.

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Authors and Affiliations

  1. School of Information Science and Technology, Hangzhou Normal University, Hangzhou, 311121, Zhejiang, China

    Xuejiao Liu & Chuanhua Wang

  2. Zhejiang Electronic Information Products Inspection and Research Institute, Hangzhou, 310007, Zhejiang, China

    Lingfeng Huang

  3. College of Computer Science and Technology, Zhejiang University, Hangzhou, 310058, Zhejiang, China

    Yingjie Xia

Authors
  1. Xuejiao Liu
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  2. Chuanhua Wang
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  3. Lingfeng Huang
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  4. Yingjie Xia
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Contributions

The author Xuejiao Liu and Chuanhua Wang wrote the main manuscript text and Lingfeng Huang revised the proposed scheme and wrote the response letter. The author Yingjie Xia gave guidance and revised the paper. All authors reviewed the manuscript.

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Correspondence to Lingfeng Huang or Yingjie Xia.

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Liu, X., Wang, C., Huang, L. et al. ARSL-V: A risk-aware relay selection scheme using reinforcement learning in VANETs. Peer-to-Peer Netw. Appl. (2024). https://doi.org/10.1007/s12083-023-01589-4

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