# Minimum-Cost Offloading for Collaborative Task Execution of MEC-Assisted Platooning
## 1 Introduction  
With the explosive increase of vehicle terminals, new vehicular services such as 3D navigation, automatic driving and so on, which require the capability of supercomputing and mass storage, are developing rapidly. Recently, MEC technology has been proposed to solve the problem of global computing resource shortage caused by massive access of mobile devices in the fifth-generation mobile communication (5G) [1], which makes the transmission cost of mobile network lower and more efficient. MEC aims to reduce the pressure of vehicle terminals by offloading computing load to mobile edges. In the traditional wireless access network, the base station (BS) deployed on the edge of the mobile network carries out the services forwarding, but the BS does not actively analyze or respond to the user's request. With the introduction of MEC, these MEC devices deployed on the edge of the network provide information technology service environment and cloud computing capability in the wireless access network nearest to user terminals (included vehicle terminals), e.g., small cell BS, macro cell BS or access to the MEC server by wireless access points, and minimize the transmission delay. Platooning is the first step to realize automatic driving. It is the most representative case of 5G. In the platoon, the distance and speed between vehicles are controlled by automatic control system through real-time updated kinematics data [2]. Vehicles in a platoon are on the same driveway, like a train, and the distance between vehicles is fixed and very short. The higher frequency of vehicle information exchange in the platoon, the faster the maneuvering response of its members, and the morecan avoid the unstable state of the platoon. This requires very high reliability and ultra-low delay data transmission, namely, vehicles should control the delay caused by the process of information processing and exchanging within 100 ms. On the other side, platooning is able to reduce fuel consumption and gas emission, as well as safe and efficient transportation in the context of intelligent transportation system (ITS) [3]. Generally, the platoon consists of two kinds of members: one is the leader (controller) and others are the members of the platoon (including the tail vehicle, the relay vehicle) [4], as shown in Figure 1. The longitudinal and lateral positions of each vehicle in the platoon are controlled by the collected data and vehicle status information (such as speed, acceleration, deceleration and location and so on). To reduce the possibility of the status of the platoon instability, these data should exchange frequently between vehicles in the platoon. Most of the vehicle-related data are processed by the terminals that have been widely equipped on the vehicles [5]. More novel and attractive vehicle services have attracted more and more people to use them, such as 3D navigation, driverless, traffic information systems, voice processing and other vehicular networking applications. In the future, vehicles will be equipped with Augmented Reality (AR) applications that allow drivers to observe the surroundings of vehicles in windowless vehicles [6; 7]. These types of vehicle applications are typical computing resource-hungry services, requiring high density computing resources and computing costs, and will be used to the platooning for better services. EyeDentify is a typical example [8], which matches the image based on target recognition algorithm. EyeDentify could be used as the security unlock for the vehicle users, and it includes a series of steps for feature extraction to transform the original image into a feature vector, and each of the step can be offloaded. Especially when running the delay-sensitive application on vehicle terminals, the vehicle users desire a fast response. For example, AR application combines computer-generated data with physical reality [9]. AR application contains five key components: the video source, the tracker, the mapper, the object recognizer and the renderer.