As a technology integrated with Internet of things, mobile edge computing (MEC) can provide real-time and low-latency services to the underlying network and improve the storage and computation ability of the networks instead of central cloud infrastructure. In mobile edge computing-based Internet of Vehicle (MEC-IoV), the vehicle users can deliver their tasks to the associated MEC servers based on offloading policy, which improves the resource utilization and computation performance greatly. However, how to evaluate the impact of uncertain interconnection between the vehicle users and MEC servers on offloading decision-making and avoid serious degradation of the offloading efficiency are important problems to be solved. In this paper, a task-offloading decision mechanism with particle swarm optimization for MEC-IoV is proposed. First, a mathematical model to calculate the computation offloading cost for cloud-edge computing system is defined. Then, the particle swarm optimization is applied to convert the offloading of task into the process and obtain the optimal offloading strategy. Furthermore, to avoid falling into local optimization, the inertia weight factor is designed to change adaptively with the value of the objective function. The experimental results show that the proposed offloading strategy can effectively reduce the energy consumption of terminal devices while guarantee the service quality of users.

作为与物联网集成的技术，移动边缘计算（MEC）可以为基础网络提供实时和低延迟的服务，并可以提高网络的存储和计算能力，而不是中央云基础架构。在基于移动边缘计算的车联网（MEC-IoV）中，车用户可以基于卸载策略将其任务交付给关联的MEC服务器，从而大大提高了资源利用率和计算性能。然而，如何评估车辆用户与MEC服务器之间不确定的互连对卸载决策的影响以及如何避免卸载效率的严重下降是需要解决的重要问题。本文提出了一种基于粒子群算法的MEC-IoV任务分担决策机制。第一的，定义了用于计算云边缘计算系统的计算卸载成本的数学模型。然后，应用粒子群算法将任务的卸载转换为过程，并获得最优的卸载策略。此外，为了避免陷入局部优化，惯性权重因子设计为随目标函数的值而自适应地变化。实验结果表明，提出的卸载策略可以有效降低终端设备的能耗，同时保证用户的服务质量。为了避免陷入局部优化，惯性权重因子设计为随目标函数的值而自适应地变化。实验结果表明，提出的卸载策略可以有效降低终端设备的能耗，同时保证用户的服务质量。为了避免陷入局部优化，惯性权重因子设计为随目标函数的值而自适应地变化。实验结果表明，提出的卸载策略可以有效降低终端设备的能耗，同时保证用户的服务质量。