# A Novel Energy-Efficient Reservation System for Edge Computing in 6G Vehicular Ad Hoc Network
## 2 Related Works
Thus, research focuses on the impact of ML on 6G-V2X networks, discussing the opportunities, challenges, and overall vision, with a focus on the physical layer, radio resource allocation, and system security. Additionally, researchers have introduced federated learning, one of the most promising ML technologies. The objective of the research study in [36] was to optimize the gas solubility and forecast the traffic flow in vehicular networks enabled by 6G. The authors emphasize that gas solubility is an essential aspect of vehicular networks as it impacts the energy consumption, quality of service, and safety of vehicles. To optimize the gas solubility, the authors suggest a novel approach utilizing deep learning techniques. The deep learning model is trained on traffic flow data and can predict the ideal gas solubility for varying traffic scenarios. Additionally, the authors propose a traffic flow forecasting model that uses historical traffic data and deep learning techniques to forecast the traffic flow in real time. The anticipated traffic flow can subsequently be employed to optimize the gas solubility and enhance the overall performance of vehicular networks. The authors evaluate the proposed approach through simulations and experiments and observe that the traffic flow forecasting model can predict the traffic flow accurately. The optimized gas solubility improves the quality of service, energy consumption, and safety of vehicles in different traffic scenarios. Overall, the study suggests a novel deep learning-based approach for the optimization of gas solubility and prediction of traffic flow in 6G-enabled vehicular networks, which could enhance the efficiency and performance of vehicular networks and enable intelligent transportation systems in the future. [PERSON] et al. [37] present a novel bidirectional visible light communication (VLC) system and evaluate its performance via various parameters, such as the data rate, latency, reliability, and coverage for 6G vehicular communication. The bidirectional VLC system outperforms the IEEE 802.11p and LTE/5G C-V2X technologies in terms of the data rate and latency. The bidirectional VLC system can achieve a data rate of up to 10 Gbps, which is significantly higher than that of the other technologies. Moreover, the VLC system has lower latency than the other technologies, which is important for safety-critical applications in 6G-enabled vehicular networks. The protocols proposed by [PERSON] et al. [38] are designed to ensure secure communication in vehicular ad hoc networks (VANETs) enabled with 6G technology. The authors use a combination of batch authentication and key exchange techniques to improve the network efficiency and reduce the communication overhead. The authors add two new protocols for anonymous batch authentication and key exchange in VANETs enabled with 6G technology: ABKA and ABKA-R. ABKA allows multiple messages to be authenticated simultaneously, thereby reducing the communication overhead. ABKA-R is an improved version of ABKA that includes a revocation mechanism to handle compromised or malfunctioning nodes. The authors describe the message flow, security properties, and advantages of each protocol. [PERSON] and [PERSON] [39] make certain assumptions about the network and its attackers, such as the availability of public key infrastructure (PKI) and the existence of malicious nodes in the network. The paper also defines the notations and terminologies used in the proposed protocols, including batch authentication, key exchange, and anonymity. [PERSON] et al. [40] demonstrate that the proposed protocols provide strong security guarantees, including confidentiality, integrity, authenticity, and anonymity. The paper also evaluates the performance of the proposed protocols in terms of communication overhead, computation time, and memory usage. The results show that the proposed protocols are efficient and can be implemented in practice.