A Distributed Clustering Algorithm based on Coalition Game for Intelligent Vehicles
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866929753452183552 |
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| author | Yang, Weiyi Liu, Xiaolu He, Lei Du, Yonghao Chen, Yingwu |
| author_facet | Yang, Weiyi Liu, Xiaolu He, Lei Du, Yonghao Chen, Yingwu |
| contents | In the context of Vehicular ad-hoc networks (VANETs), the hierarchical management of intelligent vehicles, based on clustering methods, represents a well-established solution for effectively addressing scalability and reliability issues. The previous studies have primarily focused on centralized clustering problems with a single objective. However, this paper investigates the distributed clustering problem that simultaneously optimizes two objectives: the cooperative capacity and management overhead of cluster formation, under dynamic network conditions. Specifically, the clustering problem is formulated within a coalition formation game framework to achieve both low computational complexity and automated decision-making in cluster formation. Additionally, we propose a distributed clustering algorithm (DCA) that incorporates three innovative operations for forming/breaking coalition, facilitating collaborative decision-making among individual intelligent vehicles. The convergence of the DCA is proven to result in a Nash stable partition, and extensive simulations demonstrate its superior performance compared to existing state-of-the-art approaches for coalition formation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08416 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Distributed Clustering Algorithm based on Coalition Game for Intelligent Vehicles Yang, Weiyi Liu, Xiaolu He, Lei Du, Yonghao Chen, Yingwu Computer Science and Game Theory In the context of Vehicular ad-hoc networks (VANETs), the hierarchical management of intelligent vehicles, based on clustering methods, represents a well-established solution for effectively addressing scalability and reliability issues. The previous studies have primarily focused on centralized clustering problems with a single objective. However, this paper investigates the distributed clustering problem that simultaneously optimizes two objectives: the cooperative capacity and management overhead of cluster formation, under dynamic network conditions. Specifically, the clustering problem is formulated within a coalition formation game framework to achieve both low computational complexity and automated decision-making in cluster formation. Additionally, we propose a distributed clustering algorithm (DCA) that incorporates three innovative operations for forming/breaking coalition, facilitating collaborative decision-making among individual intelligent vehicles. The convergence of the DCA is proven to result in a Nash stable partition, and extensive simulations demonstrate its superior performance compared to existing state-of-the-art approaches for coalition formation. |
| title | A Distributed Clustering Algorithm based on Coalition Game for Intelligent Vehicles |
| topic | Computer Science and Game Theory |
| url | https://arxiv.org/abs/2503.08416 |