## 2 Related Works
Moreover, the authors discuss open research problems in computation offloading, providing valuable guidance for new field researchers. While this review provides a thorough examination of existing optimization methods, it predominantly focuses on theoretical aspects and lacks empirical validation in real-world scenarios. Most of the methods discussed require validation through practical implementations and experimentation in order to assess their efficacy in diverse network environments. [PERSON] et al. [39] offered a pioneering approach to addressing the challenges of data offloading in the context of 5G-enabled vehicular edge computing (VEC) environments. The authors introduced an innovative data offloading strategy designed to optimize overall system performance by leveraging the power of deep reinforcement learning (DRL), specifically employing a cutting-edge application known as double deep Q-networks (DDQN). This strategy demonstrates significant improvements in operational efficiency and effectively manages the complexities inherent in VEC environments, including dynamic mobility conditions and fluctuating network demands. This approach represents a substantial advancement in connected vehicular networks, drastically reducing communication overhead, lowering energy consumption, and minimizing latency. The empirical analysis showcases remarkable enhancements, including an 80% improvement in energy efficiency, a 72.6% reduction in communication overhead, and a 9.8% improvement in delay compared to existing methods. This work is a benchmark for future research endeavors aiming to enhance real-time vehicular network services and tackle the intricate challenges of data offloading within 5G-enabled VEC frameworks. While the presented work offers substantial advancements in optimizing data offloading strategies for 5G-enabled VEC systems, the efficacy of the proposed approach in real-world scenarios with varying network conditions, traffic patterns, and environmental factors remains to be fully validated. While comprehensive in addressing various energy-related challenges, these existing works often need to consider the impact of transmission range on energy consumption explicitly. The communication distance between vehicles and infrastructure elements is a crucial variable, especially in dynamic vehicular environments. Longer transmission ranges may introduce increased signal attenuation and interference, potentially affecting energy consumption and overall system performance. As a consequence, designing an optimal solution to minimize energy costs while guaranteeing high user quality of experience (QoE) is a challenging issue, and standardization efforts play a pivotal role in this trajectory. Establishing common standards enhances interoperability and provides a foundation for seamless integration of diverse components within these systems. Standardized frameworks ensure consistency and compatibility, fostering a collaborative environment where researchers and practitioners can build upon established norms. In the context of the works mentioned above, adherence to standardized protocols becomes crucial for scalability, reliability, and the broader adoption of energy-efficient practices.