A Distributed Clustering Algorithm based on Coalition Game for Intelligent Vehicles

TL;DR

This work addresses scalable VANET clustering under dynamic conditions by formulating a two-objective problem that balances cluster cooperative capacity and clustering overhead. It introduces a distributed clustering algorithm (DCA) built on a coalition formation game with novel operations and log-linear learning, and proves convergence to a Nash-stable partition. Through extensive MATLAB simulations, DCA consistently outperforms state-of-the-art baselines in both solution quality and convergence speed, including significant gains in large networks where centralized approaches struggle. The results demonstrate practical viability for intelligent-vehicle networks, enabling robust, scalable, and energy-efficient cluster management in IoV environments.

Abstract

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.

Figures (4)

• Figure 1: Diagram of the system model in VANET
• Figure 2: Clustering architecture diagram.
• Figure 3: Diagram of an example of cooperative capacity calculation
• Figure 4: The convergence curves under 100 nodes of each algorithm