Efficient vehicular networks offloading using Hybrid Localization Algorithm and Deep Reinforcement Learning

In the context of growing urbanization and increased vehicular traffic, the demand for efficient computation and location-based services is paramount. This paper proposes a pioneering solution to address the challenges of precise location services in Vehicular Networks within urban settings. The system combines a Hybrid Localization Algorithm (HLA) that integrates multiple methods for improved location accuracy with Deep Reinforcement Learning (DRL) for intelligent and adaptive offloading decisions based on real-time traffic conditions. Extensive simulations demonstrate the effectiveness of our approach in reducing response times, optimizing offloading strategies, and alleviating the burden of urban peak vehicle navigation pressure. This research paves the way for enhanced location-based services and intelligent transportation systems in urban areas.