Adaptive Genetic Selection based Pinning Control with Asymmetric Coupling for Multi-Network Heterogeneous Vehicular Systems

• URL: http://arxiv.org/abs/2411.03027v1
• Date: Tue, 05 Nov 2024 11:49:26 GMT
• Title: Adaptive Genetic Selection based Pinning Control with Asymmetric Coupling for Multi-Network Heterogeneous Vehicular Systems
• Authors: Weian Guo, Ruizhi Sha, Li Li, Lun Zhang, Dongyang Li,
• Abstract summary: This paper proposes an optimized pinning control approach for heterogeneous multi-network vehicular ad-hoc networks (VANETs) We first establish a rigorous theoretical foundation by proving the stability of pinning control strategies under both single and multi-network conditions. Building on this theoretical groundwork, we propose an adaptive genetic algorithm tailored to select optimal pinning nodes.
• Score: 8.454856509502733
• License:
• Abstract: To alleviate computational load on RSUs and cloud platforms, reduce communication bandwidth requirements, and provide a more stable vehicular network service, this paper proposes an optimized pinning control approach for heterogeneous multi-network vehicular ad-hoc networks (VANETs). In such networks, vehicles participate in multiple task-specific networks with asymmetric coupling and dynamic topologies. We first establish a rigorous theoretical foundation by proving the stability of pinning control strategies under both single and multi-network conditions, deriving sufficient stability conditions using Lyapunov theory and linear matrix inequalities (LMIs). Building on this theoretical groundwork, we propose an adaptive genetic algorithm tailored to select optimal pinning nodes, effectively balancing LMI constraints while prioritizing overlapping nodes to enhance control efficiency. Extensive simulations across various network scales demonstrate that our approach achieves rapid consensus with a reduced number of control nodes, particularly when leveraging network overlaps. This work provides a comprehensive solution for efficient control node selection in complex vehicular networks, offering practical implications for deploying large-scale intelligent transportation systems.

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