Networking Controller Based Real Time Traffic Prediction in Clustered Vehicular Adhoc Networks

The vehicular ad hoc network (VANET) is an emerging network technology that has gained popularity because to its low cost, flexibility, and seamless services. Software defined networking (SDN) technology plays a critical role in network administration in the future generation of VANET with fifth generation (5G) networks. Regardless of the benefits of VANET, energy economy and traffic control are significant architectural challenges. Accurate and real-time traffic flow prediction (TFP) becomes critical for managing traffic effectively in the VANET. SDN controllers are a critical issue in VANET, which has garnered much interest in recent years. With this objective, this study develops the SDNTFP-C technique, a revolutionary SDN controller-based real-time traffic flow forecasting technique for clustered VANETs. The proposed SDNTFP-C technique combines the SDN controller's scalability, flexibility, and adaptability with deep learning (DL) models. Additionally, a novel arithmetic optimization-based clustering technique (AOCA) is developed to cluster automobiles in a VANET. The TFP procedure is then performed using a hybrid convolutional neural network model with attention-based bidirectional long short-term memory (HCNN-ABLSTM). To optimise the performance of the HCNN-ABLSTM model, the dingo optimization technique was used to tune the hyperparameters (DOA). The experimental results analysis reveals that the suggested method outperforms other current techniques on a variety of evaluation metrics.

Automated summary

This study develops a revolutionary SDN controller-based real-time traffic flow forecasting technique for clustered VANETs, combining the scalability, flexibility, and adaptability of SDN controllers with deep learning models. A novel arithmetic optimization-based clustering technique is also proposed. Experimental results demonstrate the superior performance of the suggested method in traffic management.