Optimizing Communication in VANET: A Recurrent Neural Network-based Routing and Mobility Forecasting Perspective

Abstract

This research delves into the realm of Vehicular Ad Hoc Networks (VANETs), aiming to enhance communication efficiency through a novel approach. The study focuses on the integration of Recurrent Neural Networks (RNNs) to optimize routing strategies and predict vehicular mobility patterns. By leveraging the dynamic nature of RNNs, our proposed framework addresses the challenges posed by the unpredictable environment of VANETs. The routing optimization is designed to adapt to changing network conditions, improving data delivery and reducing communication delays. Additionally, the incorporation of mobility forecasting allows for proactive decision-making, further enhancing the overall communication performance in VANETs. Experimental results demonstrate the efficacy of the proposed methodology in achieving superior communication optimization compared to traditional approaches. This work contributes to the evolving field of intelligent transportation systems by offering a comprehensive solution for the challenges inherent in VANET communication. By integrating these advanced methodologies, our research contributes to the evolution of intelligent transportation systems, reshaping the landscape of vehicular communication and connectivity." 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 compared with the conventional technique.