Hierarchical Optimization Based Multi-objective Dynamic Regulation Scheme for VANET Topology

TL;DR

This paper introduces a hierarchical, multi-objective dynamic topology regulation scheme for VANETs that improves communication performance by balancing path length, latency, and throughput through local and global optimization strategies.

Contribution

It proposes a novel two-layer regulation scheme combining local feature aggregation with global adjustment, addressing multi-objective coordination and dynamic adaptation in VANET topology optimization.

Findings

Reduces average path length to ~4 hops

Maintains end-to-end latency around 0.01 seconds

Enhances network throughput compared to traditional methods

Abstract

As a core technology of intelligent transportation systems, vehicular ad-hoc networks support latency-sensitive services such as safety warning and cooperative perception via vehicle-to-everything communications. However, their highly dynamic topology increases average path length, raises latency, and reduces throughput, severely limiting communication performance. Existing topology optimization methods lack capabilities in multi-objective coordination, dynamic adaptation, and global-local synergy. To address this, this paper proposes a two-layer dynamic topology regulation scheme combining local feature aggregation and global adjustment. The scheme constructs a dynamic multi-objective optimization model integrating average path length, end-to-end latency, and network throughput, and achieves multi-index coordination via link adaptability metrics and a dynamic normalization mechanism. it quickly responds to local link changes via feature fusion of local node feature extraction and dynamic neighborhood sensing, and balances optimization accuracy and real-time performance using a dual-mode adaptive solving strategy for global topology adjustment. It reduces network oscillation risks by introducing a performance improvement threshold and a topology validity verification mechanism. Simulation results on real urban road networks via the SUMO platform show that the proposed scheme outperforms traditional methods in average path length (stabilizing at ~4 hops), end-to-end latency (remaining ~0.01 s), and network throughput.