Efficient Routing and Mobility Prediction in VANET Using Optimal Recurrent Neural Network

Authors

  • T. Vetrivel Research scholar, Department of Computer and Information Science, Annamalai University, Annamalainagar-608002, Tamil Nadu, India
  • T. Rathimala Assistant Professor/Programmer, Department of Computer and Information Science, Faculty of Science, Annamalai University, Annamalainagar-608002, Tamil Nadu, India

Keywords:

routing technique, mobility prediction, VANET, recurrent Neural Network and Dingo optimizer.

Abstract

The main aim of the research is to develop efficient routing method and mobility prediction for VANET.The proposed technique isan Optimal Recurrent Neural Network (ORNN). The ORNN is a combination of Recurrent Neural Network (RNN) and Dingo Optimizer (DO). In the RNN, the optimal weighting factor is selected with the help of DO. The RNN is perform efficient prediction with the assistance of DO. Based on the mobility prediction, the average delay and efficient transmission probability of each vehicle is computed based on their base station and road side units. The computation of the delay and transmission probability is computed with the base of Poisson procedure. With the consideration of the above parameters, the optimal routing is selected by proposed technique. In the analysis, the destination vehicle and source vehicle are presented in the similar location. With the optimal routing process, the delay of the vehicle is reduced. The proposed technique is implemented in NS2 platform and performance is evaluated based on the performance metrices. The proposed technique is compared with the conventional technique.

Downloads

Published

2023-10-25

How to Cite

T. Vetrivel, & T. Rathimala. (2023). Efficient Routing and Mobility Prediction in VANET Using Optimal Recurrent Neural Network. Chinese Journal of Computational Mechanics, (5), 475–485. Retrieved from http://jslxxb.cn/index.php/jslxxb/article/view/4392

Issue

Section

Articles