Research On Resource Allocation Of Industrial Internet Of Things Network Slicing Based On Stackelberg Game

In the fifth generation mobile communication(5G)network environment,different application scenarios have significantly different requirements for latency,speed,reliability and other indicators.In order to meet these diverse service requirements,wireless network slicing technology has become one of the important technical supports for 5G networks through dynamic allocation and configuration of network resources.Meanwhile,with the rapid development of Io T technology,Industrial Internet of Things(IIo T)has gradually become the focus of widespread attention.In IIo T,the demand for service connectivity increases dramatically,leading to the increasing scarcity of wireless spectrum resources.In this thesis,we study the inter-slice resource allocation strategy in IIo T around the Stackelberg game approach to solve the problems of service differentiation demand and resource scarcity.Firstly,in the IIo T scenario,this thesis proposes a Stackelberg game-based multi-network in a virtual Radio Access Network(RAN)for the differentiated Quality-of-Service(Qo S)requirements of User Equipment(UE).The proposed strategy allocates bandwidth resources for multiple networks in RANs based on the Stackelberg game.The strategy uses the network slices instantiated by the Mobile Virtual Network Operator(MVNO)as intermediate participants for the UE to request resources from the Infrastructure Provider(In P).Among them,In P can effectively sell bandwidth resources to multiple MVNOs,while MVNOs purchase bandwidth according to the Qo S demand of UEs to meet the resource demand of UEs in real-time updates.Simulation results demonstrate the effectiveness of the framework in balancing the achievable utility of In Ps and MVNOs and dynamically allocating bandwidth resources to meet the differentiated needs of different service UEs in IIo T.Secondly,the diversity of industrial production tasks and the complexity of automation systems make it difficult to adapt network resources to the business needs of industrial production sites.In this thesis,a new three-layer bi-directional communication model is established at the field level,using Mobile Edge Computing(MEC)servers for auxiliary relay transmission to reduce bandwidth consumption and data upload delay.At the same time,the problem of arithmetic purchase and arithmetic price is modeled as a Stackelberg game by using a distributed iterative algorithm with a distributed iterative algorithm and the cost of purchasing arithmetic power,taking into account the delay requirements of Ultra-Reliable and Low-Latency Communications(URLLC)devices.The optimal offloading decision for the data task of massive Machine Type Communication(m MTC)devices is evaluated by using a distributed iterative algorithm and the optimal solution is obtained using a Lagrangian pairwise decomposition method.Simulation results show that the resource allocation strategy proposed in this thesis can enable the URLLC service to access more bandwidth and reduce its data upload delay.In addition,the dynamic allocation of inter-chip network bandwidth resources enables the proposed strategy to adapt to the dynamic service demands of heterogeneous services and improves resource utilization.