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Reinforcement learning-based clustering scheme for the Internet of Vehicles

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Abstract

Clustering is an efficient technique for achieving high scalability on the Internet of Vehicles (IoV). However, the latency and overhead generated from forming and maintaining clusters are common barriers to the mass adoption of this technique. To this end, we propose an efficient clustering scheme for the IoV. Leveraging reinforcement learning, our scheme can quickly form network condition-aware clusters. In addition, our reinforcement learning-based clustering scheme (RLBC) assures dynamic and cooperative maintenance for clusters. The effectiveness of our scheme is evaluated through extensive simulations. The simulation results show that the RLBC outperforms a previously developed approach and allows for more persistent cluster heads with higher durations and stable connections with their members.

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Authors and Affiliations

  1. RIIMA Lab. Computing Department, USTHB, Bab Ezzouar, Algeria

    Hayet Zerrouki, Samira Moussaoui, Abdessamed Derder & Zouina Doukha

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  1. Hayet Zerrouki
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  2. Samira Moussaoui
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  3. Abdessamed Derder
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  4. Zouina Doukha
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Correspondence to Hayet Zerrouki.

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Zerrouki, H., Moussaoui, S., Derder, A. et al. Reinforcement learning-based clustering scheme for the Internet of Vehicles. Ann. Telecommun. 76, 685–698 (2021). https://doi.org/10.1007/s12243-021-00879-3

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  • DOI: https://doi.org/10.1007/s12243-021-00879-3

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