The swarm of Unmanned Aerial Vehicles (UAVs) has garnered considerable attention, particularly in scenarios with critical situations or limited communication infrastructure. In such cases, Mission UAVs (MUs) often be deployed in clusters to provide communication services. However, the high mobility of MUs always leads to frequent changes in swarm network topology, posing challenges for the network performance. To tackle this issue, we deploy additional Relay UAVs (RUs) with a two-stage adaptive learning-based approach. In the first stage, we employ a Delaunay triangulation-based algorithm to optimize RUs’ position and construct the initial topology. In the second stage, we implement a centralized learning and decentralized execution (CTDE) reinforcement learning framework to ensure continuous network connectivity and optimize performance throughout the task cycle. To further enhance cooperation among RUs, we introduce a sequential update technique coupled with an entropy regularization term during the policy network updates. Finally, extensive simulation results demonstrate the effectiveness of our proposed algorithms.

The rapid advancement of UAV swarm networks has enabled their widespread application across various domains, including disaster relief, environmental monitoring, and intelligent transportation. Collaboration among UAVs within a swarm is vital for efficient resource utilization and optimal performance across these diverse applications. To address diverse service demands, deploying service function chains (SFC) in UAV swarm networks facilitates the real-time implementation of services through efficient resource allocation and UAV cooperation, thereby enhancing network reliability and efficiency. However, traditional SFC deployment strategies struggle to achieve reliability and efficiency due to dynamic topology and limited resources. Additionally, Stochastic Network Calculus (SNC) derives end-to-end latency, guaranteeing quality of service (QoS) in UAV swarm networks. To navigate this issue, we propose a cooperative dynamic SFC deployment algorithm that combines hierarchical proximal policy optimization (HPPO) with an edge-enhanced dynamic graph attention network (EDGAT) for real-time network state extraction. The simulation results validate the effectiveness of our proposed algorithm, showcasing improvements in deployment success rate and long-term average revenue.

Recently, unmanned aerial vehicle (UAV) swarm networks have garnered considerable interest from both academia and industry, with applications spanning disaster response and logistics. These environments are complex and demand efficient, stable network performance under highly dynamic conditions. Clustering is a promising solution to manage UAVs by creating a hierarchical structure. We propose a distributed method, Distracted Density Peaks Clustering (DDPC), which uses local information to build a decision graph and identify density centers. Additionally, a dynamic maintenance strategy enhances adaptability, and simulations confirm its effectiveness.

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.

In recent years, the rapid growth of Internet of Things (IoT) devices and applications has posed significant challenges for existing Mobile Edge Computing (MEC) architectures. The inherent latency uncertainties in MEC architectures make it difficult to support latency-sensitive applications such as autonomous vehicles. Additionally, the increasing number of connected devices has led to substantial challenges in terms of limited throughput for MEC servers. With the recent advancements in programmable network hardware, such as SmartNICs and programmable switches, the Network-based Computing (NBC) paradigm has gained widespread attention. Leveraging line-rate processing capabilities, NBC offers a promising solution for high throughput and low latency processing. This paper aims to explore the potential benefits and challenges of incorporating NBC into existing MEC architectures. The feasibility of our proposed architecture is evaluated using two use cases, Linear Quadratic Regulator (LQR) control and Complex Event Processing (CEP), demonstrating significant improvements in latency performance.

The flying ad hoc networks (FANETs) have attracted a large amount of attention from both academia and industry. Benefiting from the flexibility, the FANETs have been widely deployed in various scenarios, ranging from agricultural production to emergency rescue. However, in FANETs, the mobility of unmanned aerial vehicles (UAVs) has led to critical challenges for the stability of communications. Especially, the routing flooding mechanism extremely limits the scalability of FANET. To overcome these technical challenges, constructing a hierarchy and clustering structure in FANETs is considered a promising solution. In this paper, we propose the fission spectral clustering (FSC) strategy for UAV swarm networks. We model the UAV clustering problem as a graph cut problem. The time-sequential attributes weight of nodes and edges will be input to the FSC algorithm. Then, it will construct the Laplace matrix and calculate the first k-th eigenvectors of it. We apply the K-Means algorithm into this feature space to cut the graph by clustering the eigenvectors. Each cluster will constantly fission with this strategy until it satisfies the size and structure constraints in the UAV clusters. Some simulations are implemented to evaluate our proposed algorithm in comparison to the other state-of-the-art solutions.

Recently, diverse emerging scenarios have precipitated a substantial surge in the variety of devices and applications, which has consequently imposed more stringent demands on Quality of Service (QoS) prerequisites. As a burgeoning emerging network measurement method, In-band network telemetry (INT), can provide detailed metrics for QoS by obtaining fine-grained network status information. However, INT only outlines device-level operations, which fails to provide an entire network view for monitoring. To address this, INT orchestration based on network topology and application requirements to achieve network-level monitoring is necessary. In this paper, we propose an INT orchestration model that efficiently measures the entire network while minimizing measuring overhead. The model outputs the probe path and collects requirements for the devices it passes through. Our method effectively reduces network bandwidth consumption caused by INT process and ensures telemetry items remain fresh. Experiment results support the effectiveness of our approach.

The integration of unmanned aerial vehicles (UAVs) with 5G networks presents a promising opportunity to revolutionize wireless communication and provide high-speed internet access to remote areas. Nevertheless, the vast quantity of data generated by UAVs requires the implementation of efficient distributed learning techniques. In this study, we present a novel hybrid approach that merges Federated Learning (FL) and Split Learning (SL) to optimize the performance of UAV swarms in 5G networks. While FL is capable of reducing communication overhead and preserving privacy, SL can enhance the accuracy of the model through the utilization of the local computational resources of each device. To realize the hybrid approach, we first locally train the model on each UAV using split learning. Subsequently, the encrypted model parameters are transmitted to a central server for federated averaging. Finally, the updated model is dispatched back to each UAV for local fine-tuning, and this cycle is repeated until convergence is achieved. The hybrid approach capitalizes on the strengths of both FL and SL to minimize communication overhead and increase accuracy. To tackle the challenge of selecting the most suitable UAVs for participation in the learning process, we propose a multiagent algorithm that considers factors such as communication latency and training time. Our experimental results indicate that the proposed approach leads to substantial improvements in communication overhead and accuracy compared to conventional methods.

The Unmanned Aerial Vehicle (UAV) swarm has attracted widespread attention from both academia and industry. It has been widely adopted in disaster recovery, military communication, agricultural production, and industrial automation. In critical situations or places where communication infrastructure is lacking, deploying a UAV swarm network is a cost-effective solution. However, considering the high speed of UAV devices, designing an effective routing mechanism has been a challenging problem. In this paper, enlightened by the recent success of multi-agent reinforcement learning, we propose a multi-agent policy gradients-based UAV routing algorithm. We adopt a centralized training and decentralized executing framework, where a centralized training platform is implemented to guide the policy updating of each UAV node. Moreover, we introduce a counterfactual baseline scheme in our algorithm to improve the convergence speed. Extensive simulation results validate the effectiveness of the proposed algorithms compared to the state-of-the-art schemes.