Research On Resource Management Strategies For Information Flow Over Industrial Internet Of Things

The Industrial Internet of Things(IIoT)is the product of the deep integration between the new generation of information and communication technology and traditional industrial networks.Based on people,machines,equipment,information systems,and control systems,the IIoT aims to realize comprehensive sensing,real-time transmission,and fast processing of industrial data,leveraging wireless communication,edge computing,and other technologies.This enables precise control of the entire design,production,management,and service chain of industry.In the future,the IIoT can be applied across various industries,with a significant increase in industrial application scenarios.The number of industrial devices is increasing and their distribution range is constantly expanding.The types of industrial services are continuously increasing,with growing differences in the demands for service and strong correlation among these services.The contradiction between massive data and limited system resources continues to deepen.Considering the diversified demands of industrial scenarios and the pressure for highefficiency and stable operation of industrial systems,resource management faces great challenges in the IIoT.This dissertation aims to investigate the information sensing,transmission,processing,and control aspects of the IIoT,and to develop resource management strategies for four typical application scenarios.To achieve this,we propose customized resource allocation optimization solutions for each scenario to meet their specific demands.The following summarizes the research work and innovative points of this dissertation.(1)Research on industrial data collection scheme in remote areas assisted by Unmanned Aerial Vehicles(UAVs).To address the challenges of limited UAV flight time and increased device transmission energy consumption caused by Non-Orthogonal Multiple Access(NOMA)technology,we establish the problem of minimizing transmission energy consumption under the constraint of complete device data collection by jointly optimizing UAV trajectory,device scheduling,and transmit power.This problem is a mixed-integer non-linear optimization problem,which is extremely difficult to solve.We propose a three-level iterative optimization algorithm,which decouples the problem into device scheduling optimization sub-problem,UAV trajectory optimization subproblem,and transmit power optimization sub-problem.Simultaneously,to propose a data collection optimization scheme,iterative optimization solutions for the above sub-problems are obtained using the Generalized Benders Decomposition(GBD),Lagrange dual method,and Successive Convex Approximation(SCA).Simulation results show that this scheme effectively reduces device transmit energy consumption while ensuring complete data collection of industrial devices within the UAV flight time.(2)Research on timely status update strategy of industrial devices under resource constraints.To address the challenge of delayed status updates of industrial devices caused by resource constraints,we propose a joint control strategy of information sampling-transmission-processing by using Age of Information(AoI)to model device status updates.By jointly optimizing information sampling,device scheduling,transmit power,and computing resource allocation,an average AoI minimization problem is established.We also propose the concept of average AoI gain and use stochastic optimization theory to decouple the problem into a problem of maximizing the average AoI gain per time slot.Furthermore,we use the big-M method to propose an online time-slot-by-time-slot optimization strategy to solve the above problem and achieve joint control of device information sampling-transmission-processing.Simulation results show that our proposed strategy effectively improves the timeliness of device status information acquired by the system.(3)Research on cache strategy to improve the correlated information timeliness of industrial applications.To address the challenges of strong correlation between multiple sensor data related to application and imbalance of sensor energy consumption and application information timeliness in industrial cache networks,we propose two cache schemes,Access Point Centric Scheme(APCS)and Request Adaptive Caching Scheme(RACS).By jointly modeling application requests,system responses,and cache decisions,we propose the application AoI oriented to information correlation,and establish the problem of minimizing the weighted sum of application AoI and sensor energy consumption.This problem is a long-term stochastic optimization problem,which is extremely difficult to solve.We propose the concept of cache decision reward and decouple the problem into a problem of maximizing the expected decision reward per time slot using stochastic optimization theory.Then,several theorems are proposed to further transform this maximization problem into a multidimensional knapsack problem that is solved by dynamic programming algorithm,ingeniously handling the challenges brought by information correlation.Finally,based on the above solution process,we design online cache strategies for two cache schemes.Simulation results show that the proposed cache strategies effectively ensure energy-efficient and timely acquisition of application information.(4)Research on optimizing quality of experience for monitoring applications under constraints of control stability for control applications.We propose a two-stage optimization strategy that tackles the challenge of control applications consuming too many resources and impacting the quality of the monitoring experience.In the first stage,control stability is ensured with minimal resource consumption.In the second stage,the remaining resources are allocated to maximize the quality of the monitoring experience.First,we employ the concept of the AoI violation probability to establish a link between control stability and the success rate of information flow transmission and establish stability constraints based on the AoI of control information flow.Second,we use the Markov chain to analyze the transmission status of control information and deduce the AoI-based control application access strategy for the two cases of perfect and imperfect channel state information.Finally,using convex optimization and Lyapunov optimization theory,we develop the offline stationary randomized access strategy and the online max-weight access strategy to maximize the quality of the monitoring application experience.Simulation results show that the proposed strategies effectively enhance the quality of the monitoring application experience while maintaining the stability requirements of control applications.