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Collaborative Caching Relay Algorithm in Vehicular Networks Based on Recursive Deep Reinforcement Learning

Published: 23 April 2024 Publication History

Abstract

The rapid growth of the Internet of Vehicles (IoV) and the increasing use of vehicle information applications has led to the recognition of mobile vehicular edge caching as a viable technology that can improve Quality of Service (QoS) and reduce latency. Numerous caching algorithms have been suggested, with the common approach being the storage of contents in Road Side Units (RSUs) as a way to offer service to users in proximity. However, the high-speed movement of vehicles and limited coverage of RSUs frequently result in caching interrupts, which lead to a decline in service quality. In response, we maximise Vehicle-to-Vehicle (V2V) collaboration to create a caching system that operates without RSU support. We introduce a Recursive Deep Reinforcement Learning Collaborative Caching Relay strategy (RDRL-CR) to address this problem. With the aim of reducing service delay within capacity constraints, the caching problem is converted into a linear programming problem, with caching decisions being made through a partially observable Markov Decision Process (MDP). This approach employs a Graph Neural Network (GNN) to predict vehicle trajectories, before selecting vehicles that can act as caching nodes using link stability metrics between them. The LSTM network has been incorporated into a deep deterministic policy gradient algorithm with the aim of achieving the ultimate caching decision.

References

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Zhaojun Lu, Gang Qu, and Zhenglin Liu. 2018. A survey on recent advances in vehicular network security, trust, and privacy. IEEE Transactions on Intelligent Transportation Systems 20, 2 (2018), 760–776.
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Salahadin Seid Musa, Marco Zennaro, Mulugeta Libsie, and Ermanno Pietrosemoli. 2022. Mobility-aware proactive edge caching optimization scheme in information-centric iov networks. Sensors 22, 4 (2022), 1387.
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Qiong Wu, Shuai Shi, Ziyang Wan, Qiang Fan, Pingyi Fan, and Cui Zhang. 2022. Towards V2I age-aware fairness access: a dqn based intelligent vehicular node training and test method. arXiv preprint arXiv:2208.01283 (2022).
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ICCIP '23: Proceedings of the 2023 9th International Conference on Communication and Information Processing
December 2023
648 pages
ISBN:9798400708909
DOI:10.1145/3638884
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 April 2024

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Author Tags

  1. Collaborative Cache Relay
  2. Mobile Vehicle Edge Network
  3. Recursive Deep Reinforcement Learning

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ICCIP 2023

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Overall Acceptance Rate 61 of 301 submissions, 20%

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