Scalable Deployment Solutions for RSUs in VANETs Using Predictive Traffic Analysis Algorithms

Efficient deployment of roadside units (RSUs) is the backbone for enhancing connectivity and data exchange in Vehicular Ad hoc Networks (VANETs). However, traditional static deployments are unable to capture the complex and dynamic nature of urban traffic, which often necessitates scalable and predictive approaches to optimize RSU placement. The conventional methods lack adaptation, resulting in issues like imbalanced coverage, high latencies, and suboptimal resource utilization. To cater to these issues, this research utilizes the novel framework of Hyper-Deformable Graph Convolution Network for advanced traffic prediction and Lotus Effect Efficiency Optimization for parameter optimization. A synergy of these methods facilitates accurate and efficient RSU deployment, calibrated to real-time vehicular dynamics. The objective would be that such a model for the scalable deployment of RSUs can minimize communication delays while maximizing coverage, ensuring cost-effectiveness, and dynamically adapting to varying traffic conditions. Simulation results present substantial performance benefits: more than 99.3% reduction in average data delivery delay, more than 99.7% improvement in vehicular coverage, and 99.4% efficiency in deployment cost, compared to traditional approaches. Furthermore, the framework proposed in this paper presented high scalability and availability during peak traffic loads as well as during normal operation. Thus, this work has presented an accurate and effective model for RSU placement, complementing current approaches in a way that preserves VANET network factors and sustainability for large scale, fast and sensible, in compliance with the character and volatility of urban traffic.