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

arXiv: https://arxiv.org/abs/2411.03027 · HTML: https://mlsanity.com/paper/2411.03027/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 61 references that are in the map.
- First posted on arXiv 5 Nov 2024.

## Builds on (most cited first, 10 of 10)

- [Ambulance Emergency Response Optimization in Developing Countries](https://mlsanity.com/paper/1801.05402/) (2018)
- [Vehicular Communications: A Network Layer Perspective](https://mlsanity.com/paper/1707.09972/) (2017)
- [On-Demand Routing for Urban VANETs using Cooperating UAVs](https://mlsanity.com/paper/1908.05905/) (2019)
- [Leader selection in Vehicular Ad-hoc Networks: a Proactive Approach](https://mlsanity.com/paper/1912.06776/) (2019)
- [Robust Finite-Time Consensus Subject to Unknown Communication Time Delays Based on Delay-Dependent Criteria](https://mlsanity.com/paper/2101.10627/) (2021)
- [Fully Distributed Optimization based CAV Platooning Control under Linear Vehicle Dynamics](https://mlsanity.com/paper/2103.11081/) (2021)
- [Vehicle Following On A Ring Road Under Safety Constraints: Role of Connectivity and Coordination](https://mlsanity.com/paper/2103.14142/) (2021)
- [Towards cyber-physical systems robust to communication delays: A differential game approach](https://mlsanity.com/paper/2109.10450/) (2021)
- [Graph-based Heuristic Solution for Placing Distributed Video Processing Applications on Moving Vehicle Clusters](https://mlsanity.com/paper/2110.13392/) (2021)
- [Multi-objective Optimal Roadside Units Deployment in Urban Vehicular Networks](https://mlsanity.com/paper/2402.18581/) (2024)

## Abstract (arXiv)

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.