Dataset Collection and Generation for data dissemination in VANETs

Data Dissemination Protocol Based on Deep Learning
We propose a deep learning (DL) approach to enhance data dissemination in VANETs. Our methodology comprises
the following key components (see Figure 1):
1. Dataset Collection and Generation: Historical and real-time network data will be collected and, if necessary,
augmented using appropriate generation techniques. This diverse dataset will include vehicular movement patterns,
traffic density, and other relevant network and packet metrics.
2. Deep Neural Network Model Training and Validation: A training and validation process will be employed to finetune
the selected deep neural network (DNN) architecture. Hyperparameter optimization and robust evaluation
metrics will ensure the model’s predictive accuracy and ability to generalize to dynamic VANET conditions.
3. Integration with Dissemination Protocol using OMNeT++: The trained DNN model will be integrated into a data
dissemination protocol designed specifically for VANETs. We will utilize the OMNeT++ network simulator to
create a comprehensive experimental