Multi-Objective Communication Optimization for Temporal Continuity in Dynamic Vehicular Networks

TL;DR

This paper introduces a novel temporal-aware multi-objective optimization framework for VANETs that enhances communication stability, delay, throughput, and reliability in highly dynamic vehicular environments.

Contribution

It formally incorporates temporal continuity into multi-objective VANET optimization and develops an enhanced NSGA-II algorithm for robust, efficient solutions.

Findings

Significant improvements in reliability, delay, and throughput achieved.

Temporal continuity stabilizes communication paths over time.

Robust optimization mitigates performance degradation under uncertainties.

Abstract

Vehicular Ad-hoc Networks (VANETs) operate in highly dynamic environments characterized by high mobility, time-varying channel conditions, and frequent network disruptions. Addressing these challenges, this paper presents a novel temporal-aware multi-objective robust optimization framework, which for the first time formally incorporates temporal continuity into the optimization of dynamic multi-hop VANETs. The proposed framework simultaneously optimizes communication delay, throughput, and reliability, ensuring stable and consistent communication paths under rapidly changing conditions. A robust optimization model is formulated to mitigate performance degradation caused by uncertainties in vehicular density and channel fluctuations. To solve the optimization problem, an enhanced Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed, integrating dynamic encoding, elite inheritance, and adaptive constraint handling to efficiently balance trade-offs among conflicting objectives. Simulation results demonstrate that the proposed framework achieves significant improvements in reliability, delay reduction, and throughput enhancement, while temporal continuity effectively stabilizes communication paths over time. This work provides a pioneering and comprehensive solution for optimizing VANET communication, offering critical insights for robust and efficient strategies in intelligent transportation systems.