## 2 Related Works
This technique uses predicted vehicle traffic flow and latency data to dynamically optimize controller location and assignment. The proposed solution took a proactive approach, anticipating changes in traffic patterns and network circumstances and adjusting controller placement and allocation accordingly. This is critical for sustaining low latency and balanced load distribution in the fast-changing environment of vehicle networks. The authors ran a performance study to validate the technique across various mobility scenarios indicative of real-world vehicular networks. They evaluated the developed system's performance regarding delay and controller load. The study compared the proactive clustering strategy to conventional and contemporary controller placement strategies. The developed solution beat previous strategies by preemptively adjusting to traffic flow estimates, reducing end-user latency, and preventing controller overload. This proactive strategy successfully manages vehicular networks' dynamic and heterogeneous character, resulting in consistent and reliable network performance. [PERSON] et al. [23] investigated a scenario for compute offloading in a MEC-assisted architecture, concentrating on three critical aspects: job upload coordination across many vehicles, task migration, and servers' heterogeneous processing capabilities. The authors created a cooperative computation offloading (CCO) problem and solved it using the probabilistic computation offloading (PCO) approach. This approach lets MEC servers schedule live tasks independently. The PCO approach changes the objective function into an augmented Lagrangian form before iteratively achieving the optimal solution utilizing a convex optimization framework. Furthermore, the authors created an extensive simulation, and the results confirmed the PCO's superiority across various scenarios, emphasizing its effectiveness in minimizing job completion delays and optimizing resource utilization in MEC-assisted vehicle networks. [PERSON] et al. [24] investigated multidimensional resource management in VANETs powered by MEC and unmanned aerial vehicles (UAVs). The authors aimed to optimize the spectrum, computation, and cache resources available at MEC-mounted base stations and UAVs. To achieve this, they formulated a resource optimization problem to be solved by a central controller. Given the lengthy time necessary to solve the optimization problem and the tight delay constraints of automotive applications, the authors employed reinforcement learning techniques. They created a solution using the deep deterministic policy gradient (DDPG) algorithm. The simulation findings showed that the technique outperformed random resource management, resulting in higher QoS satisfaction. [PERSON] et al. [25] presented an integrated architecture that addresses the issues of traffic density and network coverage in VANET. This design combines MEC and SDN technologies to improve network reliability and scalability, particularly in high-traffic environments. To boost network coverage, the authors used a device-to-device (D2D) clustering mechanism that connects orphan nodes, i.e., vehicles disconnected from the network. This clustering strategy ensures that even the most difficult network nodes remain connected, increasing coverage and reliability. The suggested architecture enables ultra-low latency applications since it provides a stable and adaptable framework. The architecture's performance improved significantly when tested under realistic settings and network scenarios. Specifically, the MEC/SDN-enabled vehicular network architecture reduced task blockage rates by up to 74% compared to the baseline implementation. These findings demonstrate the efficiency of the suggested method in controlling high traffic densities while maintaining consistent network coverage. The complexity of VANET's environment and vehicles' intrinsic mobility pose issues typical offloading solutions frequently fail to meet. [PERSON] et al.