The increase in data amount makes the traditional Internet of Vehicles (IoV) fail to meet users’ needs. Hence, the IoV is explored in series. To study the construction of freight integer linear programming (ILP) model based on fog computing (FG), and to analyze the application of the model in the optimization of the networking deployment (ND) of the IoV. FG and ILP are combined to build a freight computing ILP model. The model is used to analyze the application of ND optimization in the IoV system through simulations. The results show that while analyzing the ND results in different scenarios, the model is more suitable for small-scale scenarios and can optimize the objective function; however, its utilization rate is low in large-scale scenarios. While comparing and analyzing the network cost and running time, compared with traditional cloud computing solutions, the ND solution based on FG requires less cost, shorter running time, and has apparent effectiveness and efficiency. Therefore, it is found that the FG-based model has low cost, short running time, and apparent efficiency, which provides an experimental basis for the application of the later deployment of freight vehicles (FVs) in the Internet of Things (IoT) system for ND optimization. The results will provide important theoretical support for the overall deployment of IoV.

数据量的增加使传统的车载互联网（IoV）无法满足用户的需求。因此，将对IoV进行串联探索。研究基于雾计算（FG）的货运整数线性规划（ILP）模型的构建，并分析该模型在IoV网络部署（ND）优化中的应用。将FG和ILP结合起来以构建货运计算ILP模型。该模型用于通过仿真分析ND优化在IoV系统中的应用。结果表明，该模型在分析不同场景下的ND结果时，更适合于小规模场景，并且可以优化目标函数。但是，在大规模情况下，其利用率较低。在比较和分析网络成本和运行时间时，与传统的云计算解决方案相比，基于FG的ND解决方案所需的成本更低，运行时间更短，并且具有明显的有效性和效率。因此，发现基于FG的模型成本低，运行时间短，效率高，这为以后在物联网（IoT）系统中部署货运车辆（FV）的应用提供了实验基础。用于ND优化。该结果将为IoV的整体部署提供重要的理论支持。这为稍后在货车物联网（IoT）系统中部署货运车辆（FV）进行ND优化提供了实验基础。该结果将为IoV的整体部署提供重要的理论支持。这为稍后在货车物联网（IoT）系统中部署货运车辆（FV）进行ND优化提供了实验基础。该结果将为IoV的整体部署提供重要的理论支持。