## 6 Using MEC as Enabler for Vehicular Applications
### Platooning  
A platoon is a set of networked smart vehicles traveling together securely at fairly high speed while keeping a near-constant and small distance. This conserves fuel and reduces road occupancy. In network-assisted platooning, wireless connection ties the first vehicle (the platoon leader) to the rest of the platoon members, and members move according to the commands received by the leader. To enhance safety on roads, this application may combine vehicle identification, a collision avoidance system, and sidewise control systems. A platoon may also allow other road users to insert between the platoon members (linked vs. unlinked modes). The development of MEC, combined with 5G cellular networks, makes management of a platoon system easier, and eliminates the costs of implementing powerful computation logic in vehicles. We present some works on platooning in the rest of this section. In ref. [146], authors suggest a high-level design for MEC-assisted Cooperative Adaptive Cruise Control (CACC) longitudinal control functionality. They describe its primary elements, which will be installed on the vehicle and at the network edge, as well as their interfaces. The platooning control logic is distributed between the cars and the MEC server. The high-level controller is placed at the MEC server, whilst the low-level manager is implemented aboard the vehicle. The virtual controller app and the virtual platoon app are hosted at the MEC server to allow platooning management. According to ETSI MEC requirements, they are implemented as virtualized ME apps. These apps are fed by information shared inside the platoon and preprocessed locally by the platoon app provided by platoon cars. Both the platoon leader and members continuously transfer CAMs over V2V connections [146]. To reduce the overall energy usage of the platoon without unduly delaying processing, a new approach is proposed in [147] which simultaneously optimizes the task-offloading technique and the resource-assigning strategy. This work takes into account the computing capabilities provided to the car platooning by the MEC server as well as the unoccupied computing resources of other members of the platoon. This study employs a cooperative vehicle architecture framework that combines MEC and IoV to decide when to offload member vehicle calculations. They utilize a technique that ensures that computation tasks are completed within a specified time limit by efficiently assigning computing capabilities. They also consider an improved adaptable large neighborhood search algorithm to enhance the offloading strategy. Work [148] deals with several issues of platooning. Platoon members connect to each other using current networking approaches. Traffic movement speed is anticipated using a real-world fluid model of traffic, and crash detection application is performed when cars meet at a crossroad. The authors show that this scheme conserves energy and mitigates the carbon dioxide footprint, prevents traffic jams, and increases road usage proportion. They use reinforcement learning to train cars to seek out the shortest route to the desired position. Moreover, they consider MEC to offload the computational task to the MEC server to reach a low latency goal. In ref. [149], researchers present a novel three-layer distributed control mechanism with the support of 5G and MEC to obtain short communication delay and high reliability for platooning vehicles. It includes a core, cloud, and terminal layers. The core layer manages service requests and updates the road state for each component. Also, it considers the destination, present position, and best route for the platoon of vehicles. The cloud layer uses MEC architecture and calculates and monitors road path states globally and near vehicles. The terminal unit creates the platoon and manages the position and speed of cars. A leader-follower control mechanism is used in this study.