With the rapid development of Internet of Things technology and interactive applications, the number of terminal devices in the network is increasing, and the development of interactive applications is hindered by network delay. To solve the network delay, bandwidth, and workload requirements in the new era, edge computing came into being. Edge computing aims to implement computing, storage, communication, and other services at the edge of the network by sinking cloud services from the core network to the edge of the network.Current research studies pay less attention to the impact of edge server location on the system performance, and edge server deployment is one of the key technologies for mobile edge computing. Therefore, we take 5G macrocellular/microcellular cluster as the edge server deployment scenario, propose an equivalent bandwidth-based deployment strategy, establish a mathematical model for edge server deployment, and contract a task experience function as an evaluation index from two aspects: task time and energy overhead. Based on the analysis of the experimental results, it is verified that the deployment strategy based on equivalent bandwidth is superior to other deployment strategies in terms of terminal device task overhead.

In the Internet of Vehicles (IoV), with the increasing demand for intelligent technologies such as driverless driving, more and more in-vehicle applications have been put into autonomous driving. For the computationally intensive task, the vehicle self-organizing network uses other high-performance nodes in the vehicle driving environment to hand over tasks to these nodes for execution. In this way, the computational load of the cloud alleviated. However, due to the unreliability of the communication link and the dynamic changes of the vehicle environment, lengthy task completion time may lead to the increase of task failure rate. Although the flooding algorithm can improve the success rate of task completion, the offloading expend will be large. Aiming at this problem, we design the partial flooding algorithm, which is a comprehensive evaluation method based on system reliability in the vehicle computing environment without infrastructure. Using V2V link to select some nodes with better performance for partial flooding offloading to reduce the task complete time, improve system reliability and cut down the impact of vehicle mobility on offloading. The results show that the proposed offloading strategy can not only improve the utilization of computing resources, but also promote the offloading performance of the system.

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