Low Earth Orbit (LEO) satellite networks are characterized by dynamic network topologies and on-demand service requirements from Internet-of-Things (IoT) applications, which make efficient and intelligent flow scheduling challenging. Conventional schemes rely on static configurations or manual rules, thus making it difficult to capture and respond to diverse service demands. Moreover, they often fail to model task–resource relationships effectively, hindering the generation of real-time, executable scheduling policies. To address these challenges, we propose a knowledge-enhanced, intent-driven flow scheduling (KIFS) framework. Specifically, we design a unified pipeline that first translates user intents into precise Quality of Service (QoS) requirements. It incorporates a network state awareness module to estimate per-link bandwidth and utilization, and constructs a task–resource knowledge graph (KG) to enhance the Deep Q-Network (DQN) agent via state augmentation, action pruning, and reward shaping. Finally, the framework translates the resulting policies into standards-compliant SRv6 configurations for real-time deployment. In simulations, the proposed KIFS framework demonstrates superior performance compared to standard baselines in terms of flow success rate and QoS satisfaction.

To overcome the challenges of dynamic topology and intermittent connectivity in large-scale Low Earth Orbit (LEO) satellite networks, we propose an Intent-Driven routing control framework leveraging Segment Routing over Internet Protocol version 6 (SRv6). The proposed framework enables fine-grained control over routing behaviors while maintaining protocol compatibility through packet-level semantic intent identifiers. The novel contributions include: (1) an Intent-Driven Segment Routing framework supporting diverse routing requirements with packet-level control; (2) an adaptive Polar Region Link Handling Mechanism integrated with dynamic load balancing; and (3) a multi-path-based fault recovery mechanism with rapid convergence characteristics. Experiments on Network Simulator 3 (NS3) platform demonstrate that, compared to traditional routing protocols, the proposed framework reduces end-to-end delay by 64.1% for the Iridium constellation scenario and improves stability by 68.8% in the OneWeb constellation environment.

As the number of satellites and satellite tasks continues to increase, multi-satellite collaboration task planning faces several challenges, including high complexity, high timeliness and limited scalability. This paper presents a method for task planning based on behavior tree, which leverages modularity and hierarchical structure of behavior tree to simplify planning process and enhance timeliness. We also propose an intelligent planning algorithm combining variable neighborhood search (VNS) algorithm with backtracking search algorithm (BSA) to reduce solution space and improve convergence speed. Experimental results show that this method improves satellite task planning timeliness by 16.7%, overcomes the flexibility and timeliness disadvantages of traditional methods in complex environments, and highlights the significant advantages and potential applications of behavior tree in multi-satellite collaboration field.

Satellite networks are promising paradigms for the sixth-generation (6G) global communications. However, the current satellite network is facing novel technical challenges, such as poor dynamic adjustment capability, diverse user types, and mismatches between service demands and network resources. Therefore, we propose a more general intent detection method under the intent-driven satellite network framework to achieve more intelligent satellite network management. We analyze various characteristics such as user types, user quality of service requirements, and the air interface of different types of satellites. Then we construct an intent classification model and intent extraction model based on transfer learning to provide an intent detection method for on-demand service. Our experimental results demonstrate that the proposed intent detection method exhibits a high degree of flexibility in handling user inputs and achieves high detection accuracy.

To meet novel services and networking requirements towards the next generation applications, intent-driven network is proposed as a promising networking paradigm. It is with capabilities of intent refinement, policy generation, and state awareness. And these distinctive capabilities contribute to its wide applications to the next generation networks. However, current researches lack a generalization model of intent refinement. Additionally, it is difficult to extract available knowledge from huge raw data of the network status, and guarantee the precise generation of network policies. To solve these challenges, we present a knowledge graph-enabled intent-driven network with the digital twin, which is termed as KID in this work. In the KID, knowledge graph is utilized to represent user intents, abstract network status, and express network policies. And the digital twin is applied to validate intents as well as abstract the physical network. The KID enhances the capabilities of intent-driven networks to refine intents, contributing to the continuous assurance of accurate intent fulfillment. Finally, we present a proof of concept implementation of the KID. Simulation results verify the feasibility and effectiveness of the presented KID framework.

Satellite networks can help extend wider communication coverage and provide more types of services; and introducing service function chain (SFC) to satellite networks can enhance their flexibility and scalability. However, this highly challenges the complexity and efficiency of network service management. In this work, we first present an intent-driven satellite network service management architecture. It provides a user-oriented programmable and customizable service provisioning mechanism, which can improve the flexibility and efficiency in service delivery and provisioning. Furthermore, we elaborate an intent-driven SFC deployment scheme, which is termed as ISFC. The presented ISFC is with the intent parsing, network function virtualization infrastructure point of presence selecting, and the optimal service function path generation. Finally, we provide the ISFC deployment algorithm. And the simulation results show that the presented ISFC scheme can well satisfy user’s requirements with much lower delay.

Mobility Load Balancing (MLB) is an important use case of the self-organized networks (SON), which can transfer the load from heavy-loaded cells to light-loaded cells through the handovers of users and achieve a balanced load distribution. However, there exist several limitations in current MLB meth-ods. On the one hand, traditional MLB methods focus more on the offloading of heavy-loaded cells but ignore the service and experience of transferred users. On the other hand, the adjustment of mobility parameter may cause a large number of handover, many of which are unnecessary in fact. In this paper, we propose an intent-driven MLB (IDMLB) method to optimize the handover of users. Taking the network intent and the user intent into consideration, we design a more fine-grained handover scheme and avoid the deterioration of user experience after the handover. Finally, we simulate the IDMLB in LTE scenario and the simulation results show that the proposed mechanism can effectively reduce the number of handover.

In this paper, by establishing the probability models, we systematically and comprehensively analyze the roles of the packet retransmission, the block retransmission, and the erasure coding in the reliable transport of wireless sensor networks. And as well as the three kinds of packet level schemes, we also consider the effect of two kinds of bit level strategies, CRC and FEC. At last, based on the numeric results, the appropriate schemes for different BER (high, medium, and low) are determined, and we also present some principles that reveal profound insights in designing reliable protocols and mechanisms in wireless sensor networks.

More attention has been paid to congestion control in the emerging area of wireless sensor network (WSN). However, most research works in the past stayed at the level of algorithm design or modification, and seldom sought solutions on the viewpoint of architecture. In this paper, Active Networking (AN) technology is used to make congestion control more responsive to detect/recover congestion in WSN. We design a simple Active Backpressure (BP) mechanism to allocate bandwidth Proportional to the Size of tree (ABPS). ABPS introduces programs in each data packet that tell nodes how to react to congestion, and quickly converges to a fair and efficient rate. Finally, we evaluate ABPS extensively on a 50-node wireless sensor network. Simulation results validate the effectiveness of our ABPS.

More attention has been paid to congestion control in the emerging area of wireless sensor network (WSN). However, most research works in the past stayed at the level of the current algorithms design or modification, and seldom sought solutions on the viewpoint of architecture. In this paper, Backpressure(BP) under Active Network(AN) architecture is used to make congestion control more responsive to detect/recover congestion in WSN. We design a simple Active Backpressure mechanism to allocate bandwidth Proportional to the Size of tree (ABPS), and we present a fluid-based analytical model of ABPS using stochastic differential equations. ABPS introduces programs in each data packet that tell nodes how to react to congestion, and quickly converge to a fair and efficient rate. We demonstrate a deterministic approach to analyse the stochastic model, in which we obtain a set of ordinary differential equations from our model, and we derive the average behavior of queue length and flow throughput from the ordinary differential equations. Finally, we evaluate ABPS extensively on a 50-node wireless sensor network. Simulation results validate the effectiveness of our ABPS and match well with the theoretic analysis.