Research On Computational Offloading And Resource Allocation Strategies For Industrial Internet Of Things

The Industrial Internet of Things(IIoT)has become a key driver of modern manufacturing by enabling more efficient and intelligent production methods and driving the transformation and growth of the industry.However,the explosive growth of data from multiple sources in industrial sites and the massive volume of data from IIoT devices puts pressure on traditional cloud computing,which leads to slower data processing and increased energy consumption,hindering the optimization and improvement of industrial processes.Computational offloading,as a key technology for edge computing,can effectively solve the problems in IIoT data processing by transferring computational tasks from the centralized cloud to edge servers closer to the data source.However,some longdistance data transmission in industrial sites is subject to problems such as higher latency and insufficient coverage.In this thesis,we introduce relay collaboration technology in IIoT edge computing system to expand the coverage of edge computing in IIoT system and reduce the latency of the system.This thesis focuses on the computation offloading decision of end devices and the reasonable allocation of limited "time-space-frequency-computing" resources in the device layer.The main research of this paper is as follows:In a multi-terminal single-relay scenario of IIoT,this thesis designs a novel multilayer edge computing architecture for transmitting latency-sensitive data in IIoT.The architecture integrates relay collaboration and energy harvesting techniques to extend the coverage of edge computing,reduce system latency,and extend the lifetime of relays.In addition,considering the energy and service deadline constraints present in industrial field-level networks,this thesis proposes a computational offloading and resource allocation algorithm based on energy harvesting that uses Sequential Quadratic Programming(SQP)method to minimize the processing delay of the system.Simulation results show that the proposed twostage offloading strategy based on fine-grained energy division in energy harvesting relays can effectively reduce the system delay.In the multi-terminal multi-relay scenario of IIoT,a relay preference and computational offloading strategy is proposed in this thesis in order to meet the low energy consumption requirement in industrial sites.In addition,a mixed-integer nonlinear programming problem that minimizes the system energy consumption is constructed in this thesis considering practical constraints such as computational and communication resources existing in industrial field-level networks.The optimization problem can be decomposed into two subproblems with independent objectives and constraints by temporarily fixing the relaypreferred 0-1 strategy and calculating the offloading ratio.First,the optimal computing resource allocation scheme for relays is obtained through the Karush-Kuhn-Tucker(KKT)conditions.Then,the simulated annealing(SA)algorithm is used to determine the optimal computation offloading ratio and relay selection strategy.Finally,the effectiveness of the proposed method is verified through simulation.