With the developing of Internet of Things (IoT) and mobile edge computing (MEC), more and more sensing devices are widely deployed in the smart city. These sensing devices generate various kinds of tasks, which need to be sent to cloud to process. Usually, the sensing devices do not equip with wireless modules, because it is neither economical nor energy saving. Thus, it is a challenging problem to find a way to offload tasks for sensing devices. However, many vehicles are moving around the city, which can communicate with sensing devices in an effective and low-cost way. In this paper, we propose a computation offloading scheme through mobile vehicles in IoT-edge-cloud network. The sensing devices generate tasks and transmit the tasks to vehicles, then the vehicles decide to compute the tasks in the local vehicle, MEC server or cloud center. The computation offloading decision is made based on the utility function of the energy consumption and transmission delay, and the deep reinforcement learning technique is adopted to make decisions. Our proposed method can make full use of the existing infrastructures to implement the task offloading of sensing devices, the experimental results show that our proposed solution can achieve the maximum reward and decrease delay.

随着物联网（IoT）和移动边缘计算（MEC）的发展，越来越多的传感设备被广泛部署在智慧城市中。这些传感设备会生成各种任务，这些任务需要发送到云中进行处理。通常，感测设备不配备无线模块，因为它既不经济也不节省能源。因此，找到一种方法来减轻感测设备的任务负担是一个具有挑战性的问题。但是，许多车辆正在城市中移动，它们可以以有效且低成本的方式与传感设备进行通信。在本文中，我们提出了一种在物联网边缘云网络中通过移动车辆的计算分流方案。传感设备生成任务并将任务传输到车辆，然后车辆决定在本地车辆，MEC服务器或云中心中计算任务。基于能量消耗和传输延迟的效用函数，进行计算分流决策，并采用深度强化学习技术进行决策。本文提出的方法可以充分利用现有的基础设施来完成传感设备的任务分流，实验结果表明，本文提出的解决方案可以实现最大的回报，减少延迟。