Research On Multi-layer Edge Computing Architecture And Resource Allocation For Field-level Heterogeneous Networks

With the rapid development of the Industrial Internet of Things(IIo T),more and more devices and sensors are connected to the production network,generating a large amount of field-level business data.In order to process and analyze key data in a short period of time,Mobile Edge Computing(MEC),as a new type of computing architecture and technology,has been widely concerned and applied in the industrial field.By assigning tasks to edge nodes closer to user equipment for processing and analysis,MEC can realize real-time data processing and analysis,improve system reliability,and reduce maintenance costs and risks.In order to provide efficient,flexible and reliable edge computing services in industrial application scenarios,this paper studies the multi-layer edge computing architecture and resource allocation of field-level heterogeneous networks,and realizes efficient,flexible and reliable edge computing in industrial application scenarios Serve.First of all,in the current industrial field-level network architecture,various heterogeneous terminal devices need to consume a lot of energy and processing time when completing tasks.As the device closest to the terminal,the gateway can aggregate various network protocol data,and as an edge server,assist the terminal device to complete processing tasks.In order to maximize the use of field-level network resources,this paper designs a task offloading path optimization strategy.This strategy fully considers the actual constraints of on-site business,the communication capabilities of relay nodes,location information and network topology,and selects the optimal resource allocation strategy and task offloading path according to the task type and size.It is offloaded to the gateway,and the optimal configuration of system resources is realized on the basis of weighing system energy consumption,delay and cost.The simulation results show that the method proposed in this paper can significantly reduce the energy consumption and task completion time of edge devices,and improve the resource utilization and task processing efficiency of edge computing.In addition,edge devices usually have limitations in computing resources and storage space,which means that in edge computing environments,the design of caching strategies becomes particularly critical.In traditional caching strategies,data is usually simply placed on the nearest node,which often fails to maximize the use of edge computing resources.In order to solve this problem,this paper proposes an improved caching strategy,which fully considers the network bandwidth between nodes and the dependencies between tasks,and dynamically allocates cached data among different nodes to make more efficient use of edge computing resource.In addition,the improved caching strategy also utilizes the collaboration among multiple edge servers to effectively reduce computation time,energy consumption,and network bandwidth usage.The simulation results show that the caching strategy proposed in this paper significantly improves the execution efficiency of tasks and the performance of the system.This not only helps to improve the resource utilization of the field-level network,but also helps to reduce the energy consumption and task completion time of field terminal equipment,thereby improving the efficiency and reliability of the entire edge computing system.