Data collection and distributed task execution in Internet of Things (IoT) networks require efficient coordination among autonomous agents to handle the growing volume of sensing and computational demands. Unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) present promising candidates for these operations due to their complementary capabilities and mobility advantages. However, effective cooperation between these heterogeneous agents faces significant challenges including communication limitations, energy constraints, and suboptimal task allocation efficiency. In this paper, we aim to maximize data collection capacity, task completion rates, while minimizing energy consumption across all UAVs. We propose U2GNet, a novel UGV-assisted framework for UAV that enables efficient task offloading and resource allocation in dynamic environments by leveraging Deep Reinforcement Learning (DRL) enhanced with Heterogeneous Graph Attention Networks (HGAT). The framework employs HGAT to process local observations and information shared by neighboring agents, while Gated Recurrent Units (GRU) address partial observability by integrating historical information, and DRL optimizes the decision-making process. Simulation results demonstrate that U2GNet improves the average data collection rate and task completion rate by 16.90% and 10.81% respectively compared to the baseline HGN approach.

Mobile crowd sensing (MCS) is an emerging paradigm that enables participants to collaborate on various sensing tasks. UAVs are increasingly integrated into MCS systems to provide more reliable, accurate and cost-effective sensing services. However, optimizing UAV trajectories and communication efficiency, especially under non-line-of-sight (NLoS) channel conditions, remains a significant challenge. This paper proposes TRAIL, a Transformer-enhanced deep reinforcement Learning (DRL) algorithm. TRAIL aims to jointly optimize UAV trajectories and Reconfigurable Intelligent Surface (RIS) phase shifts to maximize data throughput while minimizing UAV energy consumption. The optimization problem is modeled as a Markov Decision Process (MDP), where the Transformer architecture captures long-term dependencies in UAV trajectories, and these features are input into a Double Deep Q-Network with Prioritized Experience Replay (PER-DDQN) to guide the agent in learning the optimal strategy. Simulation results demonstrate that TRAIL significantly outperforms state-of-the-art methods in both data throughput and energy efficiency.

Aerial computing paradigms, particularly those involving UAVs as access points and radio towers, show significant promise for local data analysis and real-time service provision in aerial access networks. However, the limited battery life of UAVs, compounded by the high-energy demands of communication tasks and prolonged computation delays, presents a significant challenge. Deep reinforcement learning (DRL) enables UAVs to autonomously optimize their operations, reducing both energy consumption and latency. Nevertheless, the prolonged learning process of DRL can lead to inefficiencies, especially in dynamic environments where the equitable participation of UAVs is crucial. To address these issues, we introduce a fairness-oriented federated learning (FL) scheme that employs importance sampling to select UAVs for training, ensuring equitable utilization of each UAV's data. Furthermore, we integrate this FL fairness scheme into the design of DRL algorithms, termed FedDRL. This algorithm jointly optimizes the computation capabilities and bandwidth allocation of UAVs to minimize system costs. Numerical results demonstrate the fairness of FedDRL in fFL networks. Specifically, compared to other state-of-the-art DRL algorithms (e.g., TD3 and DDPG), FedDRL reduces system costs by 43.82% and 49.45%, respectively.

With the advancements in technologies such as 5G, Unmanned Aerial Vehicles (UAVs) have exhibited their potential in various application scenarios, including wireless coverage, search operations, and disaster response. In this paper, we consider the utilization of a group of UAVs as aerial base stations (BS) to collect data from IoT sensor devices. The objective is to maximize the volume of collected data while simultaneously enhancing the geographical fairness among these points of interest, all within the constraints of limited energy resources. Therefore, we propose a deep reinforcement learning (DRL) method based on Graph Attention Networks (GAT), referred to as “GADRL”. GADRL utilizes graph convolutional neural networks to extract spatial correlations among multiple UAVs and makes decisions in a distributed manner under the guidance of DRL. Furthermore, we employ Long Short-Term Memory to establish memory units for storing and utilizing historical information. Numerical results demonstrate that GADRL consistently outperforms four baseline methods, validating its computational efficiency.

Data center networks include a wide variety of service categories, which leads to a complex network topology. As a result, the QoS of different service flows cannot be guaranteed. In this paper, we propose a QoS assurance mechanism based on P4 switches (P4SQA) to make full use of network resources. P4SQA includes two functions: in-network traffic classification and in-network queue management. To reduce the communication overhead and computational pressure on the Software Defined Network (SDN) controller, we offload part of the neural computation from the control plane to the data plane. A fully connected neural network-based traffic classification method is deployed in the programmable data plane to classify different traffic types. In addition, the optimized active queue management algorithm CoDeL is integrated into the P4 switch (PCoDeL) to prevent network congestion while guaranteeing QoS more effectively. The evaluation results show that traffic classification accuracy has improved, especially in terms of the F1_score, which reached 0.97, significantly better than other state-of-the-art methods. Moreover, compared to traditional queue management algorithms, PCoDeL can improve network throughput and reduce transmission delay, which verifies the effectiveness and superiority of P4SQA in ensuring QoS.

To realize flexible networking and on-demand topology reconstructing, software-defined industrial networks (SDINs) are increasingly embracing the flat structure. Similar to software defined networks (SDN), SDIN suffers from low traffic scheduling efficiency caused by large and imbalanced flows, known as the heavy hitters problem. Due to such heavy hitters, industrial networks may fail to satisfy application’s QoS requirements, which results in more severe damages. To improve flow scheduling efficiency under heavy hitters, this article introduces a novel imitation learning-based flow scheduling (ILFS) method. ILFS utilizes P4-based In-band Network Telemetry (INT) to collect fine-grained, real-time traffic data from SDIN’s data plane. In the control plane, it integrates the Generative Adversarial Imitation Learning (GAIL) model with a soft actor critic to preserve the experiences of flow, thereby better scheduling large flows. Our experiments thoroughly compare ILFS’s performance with several state-of-the-art traffic scheduling strategies. The results indicate that ILFS successfully controls the link bandwidth the utilization between 10$\%$and 80$\%$and significantly improves the average network throughput and link utilization rate.