Optimized Real-Time Decision Making with EfficientNet in Digital Twin-Based Vehicular Networks

Real-time decision-making is vital in vehicular ad hoc networks (VANETs). It is essential to improve road safety and ensure traffic efficiency and flow. Integrating digital twins within VANET (DT-VANET) creates virtual replicas of physical vehicles, allowing in-depth analysis and effective decision-making. Many vehicular ad hoc network applications now use convolutional neural networks (CNNs). However, the growing model size and latency make implementing them in real-time systems challenging, and most previous studies focusing on using CNNs still face significant challenges. Some effective models with sustainable performances have recently been proposed. One of the most advanced models among them is EfficientNet. One may consider it a family of network models with significantly fewer parameters and computational costs. This paper proposes EfficientNet-based optimized real-time decision-making in the DT-VANET architecture. This paper investigates the performance of EfficientNet in digital-based vehicular ad hoc networks. Extensive experiments have proved that EfficientNet outperforms CNN models (ResNet50, VGG16) in accuracy, latency, computational efficiency, and convergence time, which proves its effectiveness in real-time applications of DT-VANET.