## 6 Using MEC as Enabler for Vehicular Applications
### Platooning
To maximize the benefits of both the Mobile Edge Platoon Cloud (MEPC) and the task vehicle (i.e., the one requesting offloading), the relationship between them is modeled as a Stackelberg game in [150]. The authors analyze task transferring in MEPC. They recommend service charging and asset management strategies based on blockchain to increase the effectiveness of the task vehicle and Platoon Member (PM). The two strategies allow task vehicles and MEPC logical objectives to distribute resource allocation. The adoption of blockchain, meanwhile, guarantees the confidentiality and safety of data related to service operations. Study [151] presents a cooperative adapted driving for platooning by using the benefits of MEC. Two different clouds are created by wireless technology: the Global Cloud (GC) and the Vehicular Cloud (VC) [152]. The GC may provide Cooperative Adaptive Driving (CAD) vehicles with global road data, and the platoon-managed VC can provide them with live local traffic data. The controlled traffic data include location-based spatial and temporal data, including Platoon Identification (PID), vehicle ID, position, speed, speed limits on the road, etc. The Platoon Leader (PL) manages the Platoon Members (PMs), as they arrive or quit the platoon. Moreover, the PL stores sensed and collected traffic data provided by PMs. The timestamped messages are transmitted with minimum hops. Researchers, in [153], present the use of the co-simulation approach to generate a DT of a platoon, which can analyze various traffic scenarios and protocol variations. A Simulink model for vehicle movements is combined with the CACC algorithm's instantiation in the digital model. The investigation of various scenarios within the framework of a MEC- or V2V-based communication method is made feasible by the DT. Researchers, in [154], combine MEC and V2N concepts to create a platoon management service that operates as a MEC application, possibly allowing mobile network providers to offer Platform-as-a-Service (PaaS) architecture. In the platooning scheme, when the uplink spectrum is constrained, V2N communication dramatically decreases uplink interference. As it is not necessary for all cars to be in radio range of one another, a platoon can scale up to many PMs using this strategy. Platoon control moving from cars to communications infrastructure also makes it easier to integrate other ITS capabilities such as traffic and emergency monitoring and control of a multiplatoon environment. A MEC-enabled controller also offers the additional bonus of minimizing the on-board unit computation power because there is no need to compute the control law calculations anymore. In ref. [155], authors replace every PM's own controller with a centralized one that may transfer between MEC hosts based on the action-value function the platoon learns as it travels. In more detail, they create a migration negotiator for centralized platoon management utilizing MEC, combining a Q-learning algorithm with context awareness. Additionally, they adjust CACC to measure and compensate for network delays and impairments. They describe the cooperative capabilities of many Q-learning models for quick policy making with no synchronization burden. The classic computing infrastructure is reshaped as an MEPC [156], which can offer supporting capabilities and cloud-native features. Next, an MEPC-based Cloud-Edge-End (M-CEE) structure is suggested for smart, distributed, and scalable apps that incorporate cutting-edge technology such as blockchain and AI. It can address application latency constraints, solve the distributed intelligent scheduling issue, and guarantee data confidentiality. Researchers, in [157], have established a Semi-Markov Process (SMP) model. This model aims to describe the relationship between capacity deterioration and regenerating behaviors in a vehicle-platooning system. The system employs a failure strategy to safeguard the active Cooperative Downloading and Uploading (CDU) service.