Research On Congestion Control Strategy In Satellite Internet Of Things Based On DTN

The Internet of Things(Internet of Things)application is an important application scenario in the 5G system.With the development of the times,it will usher in the growth of nearly tens of billions of device access,the explosive growth of data traffic,and the emergence of new application scenarios..However,due to the complex and diverse spatial and geographic regions of IoT services,Satellite Internet of Things,as an important application scenario of 6G networks,is proposed to serve locations where it is difficult to establish reliable ground backhaul links.In order to make up for the shortcomings of satellite communication network such as long delay,unstable link and frequent link interruption,the application of DTN(Delay Tolerant Network)to the satellite Internet of Things has received extensive attention and research.However,DTN requires a large amount of permanent storage resources.Therefore,the bandwidth resources and storage resources of satellite nodes are limited,which has become a key performance bottleneck of the satellite Internet of Things.The deployment of congestion control strategies in the satellite Internet of Things can effectively solve the performance bottleneck caused by the limited resources of satellite nodes.Therefore,in the satellite Internet of Things based on the DTN architecture,how to implement effective congestion control strategies has become one of the hot research topics in the research of satellite network technology.In this paper,starting from the scenario of the satellite Internet of Things based on the DTN architecture,combined with the perspective of different types of DTN routing strategies,an in-depth study of the DTN-based congestion control strategy in the satellite Internet of Things.The main research content and scientific innovation points of the thesis include:1.Research on congestion control strategy based on reinforcement learning and Bayesian games.In the satellite Internet of Things,the limited number of satellite nodes causes each satellite node to face the dynamic access of a large number of nodes.The bandwidth of the inter-satellite link of the satellite node is limited,which will cause the link congestion of the multi-hop routing when the network traffic increases suddenly.Under-the DTN architecture,when the storage space of low-orbit satellites is limited,the storage-carry-forwarding process will cause network storage congestion due to a sudden increase in network traffic.In order to solve the above three key scientific problems,combining multi-hop routing and forwarding with DTN store-and-forward strategy,this chapter models the dynamic access of multiple nodes as a Bayesian game model from the distributed strategy.The scheduling strategy task is used to complete congestion control at the ground users through a game,so as to solve the problem of difficult scheduling caused by satellite dynamic access.The first innovation in this chapter is the use of reinforcement learning methods to play games,allowing users to balance multi-hop link transmission and DTN store-and-forward when scheduling their own services.Let all users on the same access path achieve Bayesian equilibrium and achieve the purpose of congestion control.From the effect of congestion control,this congestion control strategy can reduce the packet loss rate by about 30%.The second innovation of this chapter is to propose a limited greedy strategy to achieve the rapid convergence of the game and the stability of the equilibrium state after the congestion is lifted.From the effect of improving the convergence rate,the finite greedy algorithm can increase the convergence rate by about 60%.2.Research on congestion control strategy based on spatio-temporal connection graph and network flow algorithm.This chapter mainly examines the congestion control strategy of the Satellite Internet of Things based on the CGR(Contact Graph Routing)routing strategy.This chapter mainly focuses on the priority and delay tolerance of IoT services,and provides congestion avoidance scheduling strategies for the CGR routing algorithm.The innovation of this chapter lies in the unified modeling of storage,link,and routing as a spatio-temporal connection diagram,combining multi-hop routing with DTN storage-carry-forwarding.This chapter proposes the HCGR-MF(Hybrid Contact Graph Routing-Max Flow)congestion avoidance algorithm,which realizes congestion avoidance by scheduling storage and link resources.When examining the priority of the business,the scheduling problem is transformed into a constrained multidimensional knapsack problem.Another innovation in this chapter is to model the knapsack problem as a minimum cost flow problem of a complex network,and propose the HCGR-MC(Hybrid Contact Graph Routing-Min Cost)congestion control algorithm.Through the calculation of the minimum cost flow,the optimal scheduling strategy can be obtained for diferent characteristic services.Through simulation verification,in terms of the arrival rate,the HCGR-MF congestion avoidance strategy increases the arrival rate by about 50%compared with CGR,and increases the arrival rate by about 30%compared with multi-hop routing.From the weighted delivery rate,HCGR-MC increases the weighted arrival rate by about 20%compared to HCGR-MF.3.Research on congestion control strategy based on flow control and storage management.This chapter mainly examines the strategy of satellite Internet of Things based on infection routing.For the VACCINE immune propagation strategy,which has been studied more in infection routing,its performance is poor under the low-rate bandwidth of the network,so it is not suitable for the scenario where the satellite Internet of Things resources are limited.Therefore,this chapter designs a scheduling strategy from the perspective of storage space management,focuses on the defects of the VACCINE immune propagation strategy at low rates,improves the VACCINE immune propagation strategy,and proposes CCS-ACS(Congestion Control Strategy With Adaptive Congestion State)congestion.Strategy.The innovation of this chapter is based on the flow control algorithm and the use of dynamic thresholds to solve the problems caused by buffer expansion.Combined with active discarding,adjustment of message reception probability,and copy control actions,a congestion control strategy based on flow control and storage management is designed.Compared with the VACCINE immune propagation strategy,the arrival rate can be increased by about 10%over the entire time interval with lower network overhead.