# Task Offloading Based on Lyapunov Optimization for MEC-Assisted Vehicular Platooning Networks
## 2 Related Work  
Since the concept of MEC was proposed, experts and scholars have conducted in-depth research on it [10]. The key technologies of MEC mainly include task offloading technology, wireless data caching technology, and local offloading technology based on a software-defined network (SDN), among which MEC offloading technology is an important approach for the MEC system to realize real-time processing of terminal services [11]. Currently, task offloading assisted by MEC has been attracted by a lot of researchers. Among this research, energy consumption is one of the most concerned issues. Researchers consider maximum delay, computing resources, channel resources, power allocation, interference, etc. as optimization constraints, to minimize energy consumption [12; 13; 14; 15], to minimize cost [16; 17; 18], or to maximize server revenue [19; 20; 21], etc. References [12; 14; 15; 16] aimed at reducing the energy consumption of MEC system. The authors in [12; 14] jointly optimized offloading decisions, wireless resource allocation, and computation resource allocation, for which [12] designed a heuristic scheme to minimize energy consumption of mobile devices. In [13], [PERSON] et al. proposed a branched-bound (RLTBB) method based on linearization technology to obtain the minimum energy consumption, which can obtain the optimal or sub-optimal results by solving the precision. The authors considered the trade-off between energy consumption and delay, and proposed a power minimization problem based on the stability constraint of the task buffer [14]. In [15], an efficient and energy-saving offloading decision algorithm based on Lyapunov optimization was proposed, which could minimize the average energy consumption of mobile devices under the premise of satisfying the delay constraint. In addition, a Lyapunov algorithm can significantly reduce energy consumption at the expense of only a small portion of response time compared to local and remote execution, and it can not only optimize the energy more effectively, but also reduce the computational complexity compared with Lagrangian relaxation algorithm. The vehicle platooning were investigated in [5; 22; 23]. In [5], [PERSON] et al. proposed a platoon communication mode based on D2D technology to improve the stability and efficiency of vehicle platooning with limited spectrum resources. In addition, then, a two-stage platoon formation algorithm based on platoon leader evaluation mechanism was proposed to form stable platoons. Several key issues need to be solved in the coordinated adaptive cruise control of human and autonomous driving, such as the difficulty to adaptively control the vehicle speed and distance adjacent vehicles. In the solving of these problems, the author [22] proposed a collaborative adaptive driving vehicle cloud computing method based on mobile edge computing, which effectively avoids the shock wave generated when driving in a platoon. The authors investigated a task offloading approach for an MEC-assisted vehicle platooning, where the Lyapunov optimization algorithm is employed for solving the optimization problem under the condition of stability of task queues [24]. In [23], [PERSON] et al. studied the offloading decision of cooperative processing tasks between platooning and the MEC server. The authors transformed the task decision-making problem with minimum cost into the shortest path problem, and uses the Lagrange relaxation algorithm to solve the problem approximately. Thus far, there is still a lack of research on how to offload tasks on vehicle platooning. An MEC-assisted vehicle platooning is investigated, and each vehicle member has the ability to handle other offloading tasks. The optimal offloading strategy is achieved through the Lyapunov optimization algorithm to minimize the average total energy consumption each time.