| In recent years,with the rapid development of the mobile Internet and the Internet of Things,the construction of the military IoT has also been further promoted.In the military Internet of Things,there are challenges such as low network latency requirements,higher credibility of information interaction capabilities,and faster data processing capabilities.To meet these challenges,edge computing is applied to the military Internet of Things,so that the data of mobile devices is executed on edge servers closer to the edge of the network,to reduce network latency and reduce network transmission pressure.Edge computing is currently being gradually developed,and the technology to apply it to the military Internet of Things is not yet mature.This article will focus on the application of edge computing in the military Internet of Things,and mainly study the realization of task scheduling engines in scenarios where the amount of data transmission is large and the real-time task requirements are high in the military Internet of Things.Efficient delivery of tasks such as load and system.The main work and contributions of this article are as follows:First,two optimization strategies for task distribution data transmission in complex environments are proposed.To solve the problem of unstable network connection caused by the complex network environment in the military Internet of Things,this paper proposes a multi-task queue method based on classification to ensure the efficient delivery of tasks.At the same time,a data segmentation transmission method based on multi-path delivery is proposed,which combines the real-time information of the edge computing network to reasonably slice the task data to complete the rapid transmission of the task data.Second,a cloud task scheduling system for edge computing is designed for scenarios such as many tasks and a large amount of data transmission in the military IoT.The system is mainly responsible for task collection and scheduling.The cloud task scheduling strategy proposed in this paper is used to select the appropriate delivery path for the task,and the task delivery is optimized through methods such as multi-task queue size control and data fragment transmission to achieve the task at the purpose of delivering to the target edge server in the shortest time.Third,an edge-side scheduling system combined with an edge computing cloud task scheduling system is designed.The system is mainly responsible for the forwarding and execution of tasks,completes the forwarding of tasks according to the set strategy,and allocates and executes the tasks that need to be performed in a proper way.Through the collaboration of the cloud scheduling system and the edge scheduling system,the goal of efficient and rapid delivery of the entire task is achieved. |
