The rapid advancement of the UAV swarm network has made its widespread application across a multitude of domains. However, the inherently dynamic nature of the network often gives rise to intermittent connectivity issues, leading to a significant reduction in the data transmission capacity. To address this challenge, this study explores the integration of Information-centric Network (ICN) with the delay-tolerant network (DTN). This design aims to enhance message delivery rates by caching content data packets in UAV nodes. Building upon this architecture, we study the congestion control and load balancing problem. We design an on-demand collaborative communication routing algorithm. In our design, we first propose a routing decision model that incorporates multiple routing metrics to capture the dynamic evolution patterns of network nodes, effectively controlling local congestion issues. Subsequently, we employ Lyapunov optimization techniques to achieve a network load balancing. By integrating the Lyapunov drift function, we ensure the stability of a feasible solution space within the model. Additionally, considering the high communication overhead caused by the sparse communication characteristics of DTN, we deploy a Multi-Agent Incentivized Communication (MAIC) algorithm to optimize routing scheduling strategies. Within the MAIC framework, each agent develops unique models for its teammates to generate customized information and minimize network information redundancy. Simulation results demonstrate that this algorithm effectively ensures a congestion control and a load balancing within the UAV swarm network while maintaining communication overhead in routing computations at a minimal level.

Over the past decade, low-Earth-orbit (LEO) satellite networks have emerged as a critical infrastructure in communication systems, providing wide coverage, high reliability, and global connectivity. Recently, the development of 6G technologies has challenged the LEO satellite networks to guarantee deterministic scheduling for time-sensitive services. However, traditional deterministic networking techniques fall short for LEO satellite networks. First, these techniques impose strict time constraints, but in LEO satellite networks, delay and jitter typically range in the tens of milliseconds, which exceed these limits and render them infeasible. Second, the dynamic topologies of LEO satellite networks challenge the inflexible scheduling strategies generated by these techniques, leading to sub-optimal performance and potential strategy failures. To tackle the first problem, we propose a Cycle Specified Queuing and Forwarding (CSQF) based deterministic flows scheduling mechanism. It relaxes strict time constraints by employing cyclic multi-queue scheduling, enabling more flexible and reliable long-distance transmission. For the second problem, we propose a learning-based swarm intelligence method for deterministic flows scheduling in dynamic LEO satellite networks. It includes an algorithm that combines a Dynamic Graph Convolutional Network (DGCN) with an Adaptive Ant Colony Optimization (ACO) algorithm, referred to as the DGCN-ACO algorithm. The DGCN captures the dynamic feature of the network and generates the heuristic information. The Adaptive ACO utilizes the heuristic information and considers each flow's attribute to generate multi-path scheduling strategies for each deterministic flow, as well as updates the DGCN. The experiment results demonstrate the effectiveness of our proposed algorithm.

Cyclic queuing and forwarding (CQF), proposed in IEEE 802.1 Qch, is a practical mechanism for guaranteeing deterministic transmission for time-sensitive networks (TSNs). However, only the queue model and the workflow for terrestrial networks are defined in IEEE 802.1 Qch. To make TSNs practical for future 6G applications, a general scheduling model that maps time-sensitive flows (TSFs) to the underlying resources of low-Earth-orbit satellite-terrestrial integration networks (LEOSTINs) is urgently needed. The networking conditions of STINs are quite different from those of terrestrial networks due to the large-scale spatial coverage of STINs. Hence, in order to determine the feasibility of deploying TSNs in LEO-STINs, we evaluate the CQF performance for LEO-STINs in this paper. Then, a software-defined-network-based LEO-STIN architecture for the entire lifecycle of TSFs is designed. To address the drawbacks of the LEO-STIN scenario, we propose a cyclic priority and forwarding (CPF) mechanism to improve the performance of time-sensitive services. CPF removes the bandwidth limitation of CQF for TSFs, which makes TSNs practical for LEO-STINs. We perform a simulation of a Walker constellation to test the proposed algorithm and existing TSN techniques using OMNET ++. The results show that the proposed algorithm reduces the packet loss ratio by an order of magnitude and the service time-out ratio by 70% compared to existing mechanisms.