Mixture-of-Experts (MoE) models outperform traditional dense models through sparse expert activation, where each token is dynamically routed to a small subset of experts. Across many tokens, these sparse Dispatch operations induce all-to-all traffic, making communication a major bottleneck for both training and inference: unicast replication wastes bandwidth, while table-driven multicast struggles with receiver-set churn and incast. In this paper, we propose SprayCast, a congestion-adaptive native RDMA multicast scheme for dynamic sparse token Dispatch. To avoid maintaining multicast forwarding tables in switches, SprayCast encodes each packet’s destination node set in its packet header using hierarchical bitmaps, enabling table-free in-network replication. It uses in-band network telemetry (INT) feedback to steer replication away from congested multicast branches and range-based negative acknowledgments (NACKs) for localized loss recovery, saving bandwidth and reducing tail latency in dynamic all-to-all communication. In htsim simulations on a 128-server fat-tree, SprayCast achieves better scalability as top-K dispatch fanout increases and reduces P99 dispatch tail latency by up to 6 × at K = 8 compared with representative baselines.

QoE metrics for video provides network operators with insight into the quality of service of their video delivery, giving them valid information to optimize bandwidth resource allocation. However, with the popularization of end-to-end encryption protocols (e.g., SSL/TLS), operators cannot directly obtain valuable information from encrypted traffic. In this paper, we present MultiQoE, which leverages multimodal features with multihead attention mechanism, enabling more accurate and wide-ranging real-time DASH video QoE measurements. We carefully select round-trip time (RTT) and throughput (THR) as multimodal input features so as to capture complementary information. Building on this, we develop a robust deep learning architecture that integrates convolutional neural network for effective feature extraction and multihead attention mechanism for enhanced contextual understanding. This combination allows the model to process complex relationships between the input modalities and deliver more precise measurement related to video QoE metrics. We evaluate MultiQoE on the real-world DASH traffic dataset collected from our platform, and it outperform existing methods in QoE measurement across four tasks. Resolution and rebuffering time classification improve by 2% and 0.54%, while MSE for rebuffering duration and end time decrease by 4.32% and 1.54%, respectively.

Congestion control is the main method to solve network congestion. The Bottleneck Band-Width and Round-Trip ropagation time(BBR) congestion control algorithm, proposed by Google in 2016, can achieve lower latency while maintaining higher throughput. However, in high-bandwidth, long-delay network conditions, BBR and other improved algorithms suffer from low bandwidth utilization and slow convergence. In order to ameliorate the above problems, the QM_BBR algorithm proposed in this paper, improves the transmission performance of each phase by 1) Improving the speed of the Startup phase based on comparison, 2) Adjusting the performance gain of the ProbeBw phase based on adaptation, 3) Adding a new Quick-Measurement phase based on the state judgment, which can adaptively adjust the pacing gain according to the current network latency and the network congestion to make the network congestion end more quickly. The experimental results show that QM_BBR improves the convergence speed by up to 18%, reduces the retransmission by 77%, and increases the throughput by 8.1% compared with BBR.

Cache-enabled networks present challenges in managing rapidly changing information demand and accommodating diverse user preferences. This paper proposes a cache placement strategy named SMAC. SMAC is specifically tailored for video scenes and comprehensively considers the spatiotemporal characteristics of contents. By deeply analyzing the characteristics of data across three dimensions: platform, style, and theme of videos, SMAC can accurately capture and predict demand patterns. Additionally, SMAC introduces a cache threshold adaptive adjustment mechanism based on a sliding module. The mechanism dynamically adjusts the content placement level of caching according to changes in user preferences over time. Experimental results indicate that, compared to some common strategies, under various experimental conditions, SMAC can increase the hit ratio by 3-8%, reduce server load by 5-30%, and decrease total delay by 3-22%. The demonstration of these network performance validates the effectiveness of the SMAC.

The deployment of UAVs as aerial base stations (BSs) has been considered as a promising supplement to the ground networks, which can quickly build an emergency communication network in a disaster area or significantly relief the communication burden imposed by hot-spots. However, the application of UAVs as aerial BSs is constrained by the limited onboard energy and communication coverage of UAVs. In particular, for a large target area, multiple UAVs should be deployed to meet the communication requirements. Therefore, designing the optimal trajectories of multiple UAVs is crucial to boost the UAV network performance. Inspired by the promising future of UAV BSs, this paper aims at proposing a distributed 3-dimensional (3D) trajectory design algorithm for multiple UAVs to optimize the system performance. We formulate the trajectory design problem as a multi-objective optimization problem to improve the user equipment (UE) access rate, ensure fair access opportunities, increase transmitted data volume and reduce energy consumption. Further inspired by the decision-making ability of deep reinforcement learning (DRL) in complex environments, we propose a DRL based trajectory design algorithm for multiple UAVs, namely DMTD, in which UAVs can explore both the optimal flight altitude and the potential UE distribution area in the iterative interactions with the environment, and then select the optimal flight trajectories to boost the network performance from multiple aspects. Extensive experimental results under different UE distributions have demonstrated that the proposed DMTD algorithm can find the optimal altitude to provide maximum coverage. Moreover, DMTD beats existing algorithms by providing high UE access rate, ensuring fair network service and increasing total transmitted data volume at the cost of a relatively low energy consumption. Especially in the scenes with dense and randomly distributed UEs, DMTD provides a UE access rate close to 0.9 and transmits 6 times of data volume than existing algorithms.

Widespread deployment of Small Satellite Networks (SSN) fosters the foreseen integration of space-air-ground networks to provide worldwide Internet access to oceanic and remote airspace. However, the dynamic topology of SSN, the lossy wireless link, and the limited transmission resources induce unprecedented challenges to Deterministic Satellite Network Transmission (DSNT) for the sake of improving the utility of the SSN facility. Motivated by these challenges, this work aims to develop a Deterministic Satellite Network Transmission approach with deterministic Spatio-temporal routing, Redundant coding and Multipath scheduling (DSNT-SRM) for bolstering superior communications of SSN. DSNT-SRM uses the ephemeris information and dynamic resource update mechanism to predict all upcoming communication opportunities and construct the deterministic spatio-temporal routing paths. By combining sparse and redundant network coding mechanisms, DSNT-SRM no longer cares about the arrival of each packet, but the number of coded packets it receives, since the lost packets can be compensated with deterministic redundant traffic. Moreover, the adaptive traffic balance between multiple spatio-temporal paths is designed to provide a deterministic delay guarantee when facing limited node resources and multi-user competition. Extensive experiments show that DSNT-SRM can achieve satisfactory performance improvement in reducing delay and improving delivery rate.

Self-piercing riveting (SPR) is widely used for joining lightweight and dissimilar materials in automotive body manufacturing, the quality of which directly affects the safety of vehicles. However, there is still no reliable method that can be used for SPR quality control without destructive test and manual intervention. This article presents an online nondestructive SPR defect detection method based on deep learning. By learning the temporal dependencies of punch force varying with rivet displacement under different joint combinations, the proposed method can provide real-time defect alarms and avoid the enormous cost of joint dissection. We develop an SPR parameter selection mechanism to rule out the irrelevant parameters, which enhances the learning performance. For the problem of model overfitting caused by the savage imbalance of SPR data, we design a conditional generative adversarial network based data generation model. In order to accommodate the difference in defect patterns between factory and laboratory, we devise a transfer learning based model migration method, which substantially reduces the amount of labeled factory data for model training. The evaluations on real SPR data collected from two car assembly lines of Audi and NIO verify that the proposed method achieves a high detection accuracy and a low missing rate in SPR defect detection.

Information-Centric Networking (ICN) has the characteristics of in-network caching, which can reduce the transmission of duplicate traffic, reduce the load on the servers and improve the user experience. From a technical point of view, it is a very promising network architecture. A reasonable pricing mechanism can encourage internet service providers, content providers and users to participate in the operation and use of ICN, and convert ICN technical advantages into economic benefits, thereby promote the large-scale deployment of ICN. The current research focuses on ICN pricing to analyze the pricing mechanism on the internet service provider (ISP) side and the corresponding market equilibrium results. But the model of content providers (CPs) is usually relatively simple in this research. The model assumes the existence of one single CP operator, which will be very different from future deployment scenarios. Multiple CPs will introduce competition and stimulate end users to use ICN networks and ISPs to deploy ICN networks. Moreover, the relationship between CPs is not only competitive but also cooperative. This paper focuses on the complex relationship of competition and cooperation among multiple CPs, solves the non-cooperative game model based on game theory, and studies the interaction between cache and pricing strategies of ICN entities. The optimal cache share of ISPs and the optimal pricing of ISPs and CPs are obtained by establishing the optimal utility function of each entity. Finally, numerical analysis is performed to derive the utility function of ICN entities as the critical pricing and caching parameters change, while verifying the consistency with the equilibrium solution.

The volatile, covert and slow multistage attack patterns of Advanced Persistent Threat (APT) present a tricky challenge of APT detection, which are vital for organisations to protect their critical assets. In this article, we aim to develop system that aggregates and uses existing systems’ alerts to detect APTs. In order to achieve this, we propose a causal correlation aided semantic analysis system, calledPoirot, for detecting the multi-stage threats over a long-time span from existing systems’ alerts.Poirotis capable of autonomously mining causality between anomalous events, which instructs us in reorganizing the original alerts and in constructing alert-chains. The system further exploits the Latent Dirichlet Allocation (LDA) to model the semantic context of the alert-chains. This LDA model facilitates us to carry out the semantic analysis for capturing the latent attack intent as well as for reconstructing the APT scenario. We use an alert dataset provided by a cyber security company to verify the proposedPoirotin terms of the detection accuracy and the capability of attack scenario reconstruction. The experiment results are presented to show the achievable performance of the proposed semantic analysis based APT detection.

Low Earth Orbit (LEO) satellite networking has been an indispensable and promising concept for extending the Internet coverage of future space-air-ground integrated networks to oceanic and remote airspace. However, the topology dynamics of the LEO Satellite Network (LEO-SN) for network state perception (Liet al., 2019) and the intermittent nature of the Inter-Satellite Links (ISLs) for multipath routing discovery (Wanget al., 2019, Jianget al., 2019) both induce new complicated challenges to multipath traffic scheduling for the sake of improving the utility of the LEO-SN facility (Songet al., 2014, Zhanget al., 2018, and Yanget al., 2020). Motivated by these challenges, this paper aims to develop an AI aided intelligent multipath traffic scheduling approach for bolstering autonomous and efficient communications of LEO-SN. To achieve this, we formulate the multipath traffic scheduling problem into a pheromone incentivized Markov Decision Process (MDP) by considering ant routing protocol and adapting pheromone to LEO-SN. Employing enhanced pheromone characterizing network state, we propose ant-inspired multipath routing discovery, which is capable of promptly discovering routing paths available in the dynamic topology. To improve the utility of these discovered routing paths, we employ deep deterministic policy gradient into the pheromone-incentivized MDP-based scheduling problem to derive an intelligent multipath traffic scheduling strategy. The experimental results are further presented to show the achievable performance improvement.