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

作　　者：T.S.Balaji S.Srinivasan 

机构地区：[1]Saveetha School of Engineering,Saveetha Institute of Medical and Technical Sciences,Saveetha University,Saveetha Nagar,Thandalam,Chennai,602105,India [2]Department of Electronics and Communication Engineering,College of Engineering and Technology,SRM Institute of Science and Technology,Vadapalani Campus,Chennai,600026,India [3]Department of Biomedical Engineering,Saveetha School of Engineering,Saveetha Institute of Medical and Technical Sciences,Saveetha University,Saveetha Nagar,Thandalam,Chennai,602105,India

出　　处：《Intelligent Automation & Soft Computing》2023年第2期2189-2203,共15页智能自动化与软计算（英文）

摘　　要：The vehicular ad hoc network(VANET)is an emerging network tech-nology 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 withfifth generation(5G)networks.Regardless of the benefits of VANET,energy economy and traffic control are significant architectural challenges.Accurate and real-time trafficflow 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 trafficflow forecasting technique for clustered VANETs.The proposed SDNTFP-C technique combines the SDN controller’s scalability,flexibility,and adaptability with deep learning(DL)mod-els.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 atten-tion-based bidirectional long short-term memory(HCNN-ABLSTM).To optimise the performance of the HCNN-ABLSTM model,the dingo optimization techni-que was used to tune the hyperparameters(DOA).The experimental results ana-lysis reveals that the suggested method outperforms other current techniques on a variety of evaluation metrics.

关 键 词：VANET trafficflow prediction clustering metaheuristics SDN controller deep learning 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]