Yu Guo, Shenghao Ye, Shuangwu Chen, Zijian Wen, Tao Zhang, Bai Qirui, Dong Jin, Yunpeng Hou, Huasen He, Jianyang, Xiaobin Tan. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Zijian Wen, Tao Zhang, Shuangwu Chen, Shenghao Ye, Yu Guo, Qirui Chen, Jingxian Shuai, Yunpeng Hou, Huasen He, Jianyang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Shenghao Ye, Yu Guo, Dong Jin, Yuxiang Wang, Yikai Shen, Yunpeng Hou, Shuangwu Chen, Jianyang, Xiaofeng Jiang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Motivated by the intelligent decision-making ability, Deep Reinforcement Learning (DRL) has been extensively applied in multi-Unmanned Aerial Vehicle (UAV) trajectory planning. This article investigates the application of DRL in Three-Dimensional (3D) trajectory planning in environments with obstacles, where multiple UAVs act as aerial Base Stations (BSs) to provide services to ground user hotspots. The existing DRL-based algorithms require a significant amount of trial and error iterations to obtain sufficiently high-performing agents. To cope with this drawback, we introduce Transfer Learning (TL) into DRL, enabling the UAV agent to possess prior knowledge upon initialization, thereby quickly adapting to unfamiliar environments and significantly improving performance. Considering the limited local observations of UAVs, a multi-modal fusion autoencoder is proposed for extracting and compressing cross-modal features from global observations to obtain the global fusion state, which enhances the perception capabilities of UAVs without incurring excessive communication overhead. Finally, we propose an exploration novelty-driven collaborative trajectory planning algorithm for multiple UAVs, which ensures obstacle avoidance and enhances the UAVs’ exploration capabilities to cover all hotspots. We adopt a probabilistic channel model and discretize both the time and DRL action space to achieve a balance between practicality and tractability. Extensive experiments demonstrate that our proposed transfer reinforcement learning method can improve the initial performance of the UAV agents by 76%. The perception and exploration-enhanced trajectory planning algorithm significantly increased the exploration efficiency and improved hotspot coverage by 50%.

Logical anomalies occur when a product's assembly violates prescribed logical rules, which widely exist in industrial assembly and packaging processes. Due to the difficulty in comprehending such complex logical relationships, a paucity of research has focused on the industrial logical anomaly diagnosis (LAD). Recently, large language models (LLMs) have demonstrated strong semantic understanding and zero-shot reasoning capabilities, making them a promising tool for LAD. However, directly applying LLMs to LAD still face two critical challenges: 1) the scarcity of abnormal samples in real-world settings, and 2) the propensity of LLMs to generate hallucinated or unreliable diagnostic conclusions. To address these challenges, we propose LogiDiag, a novel diagnostic reasoning method for LAD, to pinpoint where and why a product fails to comply with the logical rules, thereby elevating product quality and reducing remedial intervention cost. We design a visual descriptor that identifies product component attributes even in out-of-distribution abnormal images and organizes them into component descriptions for LLMs' comprehension. To mitigate hallucinations, we devise a planner to guide LLMs in diagnostic reasoning through rule orchestration, tool allocation, and chain-of-diagnosis generation. Experimental results on multiple benchmark datasets validate the competitive performance of LogiDiag.

The exponential surge in network traffic has imposed significant challenges on traditional Internet architectures, resulting in high latency and redundant transmissions. Cache-enabled networks alleviate these issues by deploying content closer to end-users, making the planning of such networks a research focus. However, regional heterogeneity in user demand and caching interdependencies among hierarchical nodes complicate the planning process. Most existing approaches rely on simplistic even allocation or empirical methods, which fail to simultaneously meet user performance expectations and minimize deployment costs. This paper proposes a network planning framework based on the Equivalent Characteristic Time Approximation (ECTA). The approach begins by establishing a performance–resource mapping. Using ECTA, we decouple the tightly coupled characteristic time relationships across hierarchical nodes, thereby accurately estimating the required cache capacity and bandwidth needed to achieve user performance targets. Building on this foundation, we formulated the network planning as a constrained convex optimization problem that minimizes deployment cost while satisfying user performance constraints. We conducted extensive experiments on a large-scale simulation platform (ndnSIM) and a real-world cache-enabled network testbed (CENI-HeFei). The results demonstrate that, under identical network topologies and total resource constraints, our method significantly improves cache hit probability while reducing deployment costs compared to homogeneous resource allocation schemes. This work provides a practical theoretical foundation and valuable insights for the design, deployment, and optimization of future cache-enabled networks.

The massive parameter scale of sparsely-activated Mixture-of-Experts (MoE) models necessitates distributed training with hybrid parallelism. Placing such training tasks,i.e.mapping the logical partitions of an MoE model to available physical NPUs, is challenging. Due to the bandwidth and latency discrepancies between intra- and inter- Pods, the cross-Pod communication usually becomes a bottleneck. The high dispersion of NPUs in multi-tenant clusters exacerbates this issue further. However, a paucity of studies has considered the cross-Pod model placement problem. To address this challenge, we propose a novel model placement scheme tailored for MoE model training with hybrid parallelism in multi-tenant clusters. By quantifying the cross-Pod communication overhead incurred during MoE model training, the model placement is formulated as a 0-1 integer quadratic problem, which is NP hard. Motivated by the traffic difference between different parallelism, we decompose this problem into two subproblems. To solve the subproblems, we propose a lightweight two-stage algorithm based on Best-Fit strategy and neighborhood search. Experiments under different models and network topologies show that our model placement scheme can reduce cross-Pod traffic by 35.9% and cut communication time by 18.7% compared to state-of-the-art methods.

Trajectory planning for unmanned aerial vehicle (UAV) formations has garnered significant research attention due to its potential to enhance UAV-assisted communications. While deep reinforcement learning (DRL) has been widely adopted for UAV trajectory planning owing to its strong learning and decision-making capabilities, existing DRL-based algorithms suffer from slow convergence and high training cost. To address these problems, this paper proposes a novel hierarchical control framework for UAV formation trajectory planning, where a leader UAV determines the global trajectory while follower UAVs dynamically adjust their relative trajectories. We further design a multi-imperfect expert guided DRL algorithm to substantially improve the learning efficiency of the LUAV agent, enabling rapid adaptation to unfamiliar environments. Additionally, an artificial potential field (APF) based coordination mechanism is integrated to ensure safe navigation and maintain formation for follower UAVs. Experimental results demonstrate that the pro-posed algorithm achieves 100% hotspot coverage while reducing convergence time by 82% compared to state-of-the-art methods.

Most existing routing algorithms for Low-Earth-Orbit (LEO) satellite networks neglect the spatio-temporal features inherent in the network states, leading to suboptimal performance in scenarios with dynamic network topologies. In this paper, we propose a Spatio-Temporal Graph Attention Network (STGAN) architecture for the extraction of spatio-temporal features from satellite network states. Building upon this, we propose a State Prediction based Dynamic Routing Algorithm (SP-DRA), which combines STGAN with the enhanced Shortest Path First algorithm (eSPF). SP-DRA utilizes STGAN to predict future network states by exploiting the spatio-temporal correlations of historic observations. Based on state predictions, each link is assigned with a delay-based prediction weight, which is used as the input for eSPF. The proposed eSPF dynamically selects the path with the smallest prediction weight to facilitate congestion avoidance and reduce transmission delay. Simulation results show that our STGAN architecture provides up to 18.6% prediction accuracy improvement than the existing deep learning-based methods, namely STGCN and ST-MGAT. Meanwhile, the SP-DRA algorithm outperforms existing routing strategies including SPF, Explicit load balancing algorithm (ELB), and Satellite networks Link State Routing algorithm (SLSR) in terms of packet loss rate and average end-to-end delay. Specifically, the performance gains increase with network traffic.

Satellite networks have been proposed as a vital component in 6G networks for providing global connections. In recent years, satellite network traffic has been increasing. However, the limited onboard resources and inter-satellite link bandwidth make network congestion a critical issue. In order to avoid network congestion and improve quality of service, satellite traffic prediction has received more attention. However, the traditional forecasting models do not fully consider the dynamic topology of satellite networks and the spatial-temporal features of traffic. Thus, we propose a novel traffic prediction method for dynamic satellite networks. Considering that there is traffic correlation between nodes without direct connection, our model introduces a graph generation module to generate adjacency matrices based on dynamic attributes. Moreover, to improve the accuracy of prediction, we further propose the spatial attention module and time sequence processing module to exploit the temporal and spatial correlations of satellite traffic respectively. The experimental results show that our method outperforms the compared algorithms and increases up to 6.84 % prediction accuracy.

Fault detection (FD) is essential for wafer quality control in semiconductor manufacturing (SM), as it can identify abnormal wafers in the early stages. However, frequent chamber discrepancies leads to distribution shifts in the data from different chambers, which causes performance degradation of the existing model. In this paper, we conceive a robust cross-chamber fault detection method in SM, which to the best of our knowledge is the first work that employs domain generalization to address the issue of cross-chamber fault detection in SM. Our basic idea is to map the samples from the source chambers and target chambers to the same hypersphere space, making normal samples cluster around a specific feature center while faulty samples stay away from it. Due to the scarcity of faulty samples, we propose a one-class classification-based fault detection method relying on normal samples to establish classification boundaries. To generalize the model to unseen target chambers, we design a meta-learning-based one-class domain generalization approach. We also devise a strategy to enhance distribution alignment within hypersphere, making the classification boundaries of various chambers be close to each other. The evaluations on real-world data collected from a wet process equipment in SM verify the high robustness of the model to chamber discrepancies with limited faulty samples.

The integrated merits of Unmanned Aerial Vehicle (UAV) networks including high mobility, ease of deployment and low cost have promoted their widely application in both civilian and military areas. However, the complex communication environments, dynamic network topology and intermittent links pose significant challenges to the transmission reliability of UAV networks. Existing end-to-end reliable transmission mechanisms rely on feedbacks from receivers to trigger retransmission, which impose extra transmission delay and redundant retransmission. In this work, we propose a Hop-by-Hop Redundancy-guaranteed Adaptive Coding (HHRAC) approach for enhancing transmission reliability of dynamic UAV Networks. To cope with lossy links, a link quality-adaptive coding algorithm is proposed, which dynamically adjusts coding redundancy rate according to link quality. Meanwhile, the Cauchy matrix is employed to design efficient coding matrices, which greatly improve decoding efficiency and enable the intermediate nodes to perform low-complexity verification. Moreover, a redundancy-guaranteed hop-by-hop transmission mechanism is provided to avoid End-to-End (E2E) retransmission and ensure the destination node has a high probability to receive sufficient packets. To avoid receive queue overflow, we further propose a queue length prediction based congestion control algorithm to control the sending rates of UAVs. The experimental results show that HHRAC achieves significant performance gains compared to existing algorithms in terms of transmission delay and retransmission times.

Space-Ground Integrated Networks (SGINs) hold the potential to enable seamless and high-quality global coverage in an economically viable manner. However, the limited bandwidth and relatively long delay of satellite-ground links pose significant challenges in meeting the increasing demands driven by surging traffic. To address this issue, we propose a three-tier cooperative caching architecture incorporating base stations (BSs), satellites, and a content server, which aims to reduce the average content retrieval delay through cooperative caching between BSs and satellites. The joint optimization problem among them is both challenging to solve and non-scalable due to its exponentially increasing computational complexity. Meanwhile, the geographic characteristics of requests are overlooked in existing work. As a consequence, we propose a novel block partitioning algorithm based on the popularity similarity across areas to facilitate cooperative caching, which reduces the computational complexity, and ensures the scalability and the effectiveness of cooperative caching. Based on the partitioned blocks, the optimization problem is decomposed into two subproblems: intra-block cooperative optimization among BSs and inter-block cooperative optimization among satellites, effectively catering to the characteristics of wide-area coverage in SGINs. For the subproblems with finite dimensions, Semidefinite Relaxation (SDR) based intra-block and inter-block cooperative caching approaches are proposed to obtain the optimal cooperative caching strategies. Extensive simulations demonstrate that, compared to directly solving the original problem, the proposed algorithm reduces the solving time from exponential to linear growth. Moreover, our algorithm outperforms existing schemes by reducing 13% average retrieval delay and improving 12% overall cache hit rate.

Low Earth Orbit Satellite Networks (LEOSNs) have emerged as a promising paradigm for space information networks, where multiple inter-satellite links facilitate the rapid transmission of on-orbit data to multi-ground station systems. When the destination of the transmission is not a certain ground station, but anyone of the ground stations, it can be modeled as an anycast problem. However, the time-varying topology, dynamic inter-satellite link status and limited onboard resources bring challenges to the routing of on-orbit data. Existing unicast routing solutions failed to address the anycast routing problem as they could not fully utilize multiple ground stations. Inspired by the Potential Field (PF) theory in physics, we make the first attempt to adopt the PF approach in the on-orbit data anycast routing problem. We design a synthesized PF model including several sub-fields corresponding to network states such as length of path, node transmission load and link bandwidth. By implementing inter-satellite propagation and synthesis of PF, dynamic perception and unified measurement of network states can be achieved. Based on our PF model, we propose a distributed PF-based and Network State-aware Anycast Routing (PFNSAR) algorithm, which regards the ground stations as multiple sources of attractive potential and guides packets along the gradient of synthesized PF. Meanwhile, the occurrence of the well-known local minimum is novelly handled by setting PF configuration subject to a parameter constraint and switching routing modes when routing packets. Extensive simulations demonstrate that PFNSAR provides unified assessment of multi-dimensional network states, reduces up to 30% of average delivery delay, and improves the performance including packet delivery rate and load balancing compared with existing works.

Semantic communication is an emerging paradigm with significant potential for image transmission. However, resource-efficient architecture design and resource allocation in this field have not received adequate research attention. This paper proposes a resource-efficient multi-branch semantic communication architecture based on saliency detection, aimed at optimizing computational efficiency in image transmission. The architecture leverages models with varying capacities to process regions of images with different complexities. We further address the problem of multi-user uplink semantic communication and resource allocation, focusing on minimizing the total energy consumption for communication and computation. The optimization problem, subject to user demand, computation, delay, and transmission power constraints, is non-convex due to the coupling of variables, making it challenging to solve. To tackle this, we introduce a two-level decomposition approach. The lower-level problem, given a fixed compression rate, is solved using Karush-Kuhn-Tucker (KKT) conditions to derive closed-form solutions for transmission power and computation frequency. The upper-level problem, which optimizes the compression rate, is reformulated as a monotone optimization problem for efficient solution finding. Numerical results demonstrate that the proposed architecture significantly reduces computational resource usage while maintaining image quality, and the resource allocation strategy effectively minimizes energy consumption, outperforming baseline schemes in terms of energy efficiency.

Low Earth Orbit (LEO) satellites play a crucial role in providing high-speed internet to remote areas and ensuring network resilience during outages. The design of efficient satellite constellations requires optimizing network topology, which is a complex task due to the large solution space and the need for fault tolerance. This paper presents the AlphaSat algorithm, a two-phase approach to improve latency and network robustness in LEO constellations. In the initialization phase, Monte Carlo Tree Search (MCTS) is used to generate an initial topology by selecting links from a vast search space. In the refinement phase, an edge-switching method is applied to enhance network resilience and performance. AlphaSat is evaluated on OneWeb, Starlink, and Telesat mega-constellations, demonstrating superior performance over existing algorithms. The results show significant reductions in latency ranging from 4.7% to 44.5% and improvements in network robustness, increasing by 3.3% to 28.3%. Furthermore, AlphaSat effectively balances network load and optimizes power consumption, offering a promising solution for efficient and resilient LEO satellite network design.

The integration of edge computing into satellite networks offers a promising solution for extending computational services to remote and underserved areas. To effectively provide a variety of computing services, it is essential to cache the corresponding services on satellites. However, challenges exist such as dynamic computing requests that vary over time and space, energy constraints due to restricted power supply, as well as limited storage capacity on satellites and the impracticality of frequently adjusting service deployments. To tackle such challenges, this paper proposes a two-timescale joint optimization framework to minimize energy consumption in satellite edge computing networks while ensuring the delay requirements, by jointly optimizing service placement and task offloading, as well as computation resource and power allocation. On a larger timescale, we optimize service caching placement by strategically deploying services on satellites and ground devices (GDs) based on long-term service request statistics, aiming to minimize the total average delay over each time frame. We develop an efficient iterative algorithm by employing penalty-based methods and Lagrange duality techniques to achieve suboptimal service deployment. On a smaller timescale, we optimize task offloading and resource allocation in shorter time slots, adapting to dynamic traffic fluctuations to minimize energy consumption while meeting delay constraints. We utilize alternating optimization and quadratic transform methods to efficiently allocate resources and schedule tasks. Extensive simulations demonstrate the effectiveness and superiority of our framework over benchmark schemes, revealing significant reductions in delay and energy consumption. The results also highlight the trade-offs between task delay and energy consumption, as well as between transmit power and energy consumption.

Cellular-connected Unmanned Aerial Vehicles (UAVs) have significant potential for target tracking in future cellular networks due to their broad coverage and operational flexibility. In this paper, we consider a multi-cell cellular network with a cellular-connected UAV for target tracking, which encounters challenges such as unpredictable flight energy consumption from the stochastic movements of the tracking target and severe uplink interference from ground devices (GDs). To tackle these challenges, we propose a multi-stage stochastic optimization framework focused on energy-efficient target tracking with interference coordination. Our objective is to optimize the long-term average uplink throughput of both aerial users and GDs by jointly optimizing the UAV's trajectory, power allocation, and cell association across multiple orthogonal communication resource blocks (RBs). The formulated stochastic non-convex problem is first transformed into a deterministic problem for each time slot by using the Lyapunov optimization framework. An online optimization strategy is proposed, utilizing the optimal structure, alternative optimization, and successive convex approximation (SCA) techniques. Simulation results show that the proposed approach significantly enhances network throughput and UAV energy queue stability compared to existing baseline schemes.

Website Fingerprinting (WF) attacks have posed a serious threat to the anonymity of the onion router (Tor) communication system, as attackers can passively pry into the encrypted traffic and infer the website visited by users. To defend against WF, recent studies focus on adversarial perturbations. However, most of them suffer from a high bandwidth overhead and a low defense performance. To address this problem, our basic idea is to generate perturbation only on the sensitive regions, which can effectively mask the website’s fingerprint, thus misleading the WF attack models and reducing the bandwidth overhead. In this paper, we formulate a joint optimization problem of perturbation position and magnitude by confining the perturbations within sensitive regions, which is rarely considered in the literature. We propose a robust low-overhead WF defense method based on reinforcement learning (RL), named RLpatch. RLpatch identifies the common sensitive regions of various surrogate models and adjusts perturbation according to the query result from a query WF model. It further employs the positional frequency of perturbations to generate a common perturbation paradigm for different traces of a same website. Experimental results show that RLpatch achieves higher defense performance, lower bandwidth overhead and better robustness against adversarial training compared to the state-of-the-art methods.

Robust Privacy-Preserving Federated Learning (PPFL) is a secure distributed machine learning paradigm designed for untrusted environments, aiming to aggregate gradients while ensuring the reliability of the results without disclosing user gradients. However, existing single-server robust PPFL schemes require users to generate commitments in each aggregation round to ensure the correctness of the aggregation results, which leads to high computational overhead. We propose an efficient single-server robust PPFL scheme named Privacy-Preserving Robust Federated Learning (PRFL). PRFL achieves efficient gradient aggregation through a “detection-identification-exclusion” strategy. PRFL only performs quick detection without requiring high-complexity commitments in most of aggregation round, thereby ensuring excellent efficiency. PRFL comprises three pivotal components: Privacy-Preserving Gradient Aggregation Based on Packed Secret Sharing (PGAPS), Swift Share Verification based on Dual Codes (SSVDC), and Commitment-based Malicious User Identification (CMUI). PGAPS is utilized to implement the FLTrust rule without disclosing gradients. SSVDC swiftly detects incorrect shares without using commitments. CMUI identifies malicious users when SSVDC detects incorrect shares. Experimental results demonstrate the robustness and efficiency of PRFL. In a PPFL system with 100 users, PRFL can robustly aggregate gradients of a million dimensions within 37 seconds of average computational time.

Distributed training of deep neural networks (DNNs) suffers from efficiency declines in dynamic heterogeneous environments, due to the resource wastage brought by the straggler problem in data parallelism (DP) and pipeline bubbles in model parallelism (MP). Additionally, the limited resource availability requires a trade-off between training performance and long-term costs, particularly in online settings. To address these challenges, this article presents a novel online approach to maximize long-term training efficiency in heterogeneous environments through uneven data assignment and communication-aware model partitioning. A group-based hierarchical architecture combining DP and MP is developed to balance discrepant computation and communication capabilities, and offer a flexible parallel mechanism. In order to jointly optimize the performance and long-term cost of the online DL training process, we formulate this problem as a stochastic optimization with time-averaged constraints. By utilizing Lyapunov’s stochastic network optimization theory, we decompose it into several instantaneous sub-optimizations, and devise an effective online solution to address them based on tentative searching and linear solving. We have implemented a prototype system and evaluated the effectiveness of our solution based on realistic experiments, reducing batch training time by up to 68.59% over state-of-the-art methods.

Microservice architecture offers great scalability and flexibility to the development of online services systems. Performance anomalies, which happen frequently due to code bugs or runtime environment misconfiguration, can severely damage the system availability and cause great losses. However, it is challenging to detect performance anomalies and locate their root causes considering the large volume of monitoring data (e.g., metrics and traces) and the complex dynamic interdependence between heterogeneous services. Against these challenges, we propose HG-PAD, an automatic performance anomaly diagnosis (PAD) framework for microservice systems. We build the multi-relation heterogeneous graph to model the intricate dependency between services. We further design a structure learning mechanism combining graph neural network (GNN) and node embedding learning to capture the dynamic and latent dependencies. Based on the optimized dependency graph, we devise a Conditional Variational Auto-Encoder (CVAE) based unsupervised anomaly detection method and a graph attention network (GAT) based root cause localization method for accurate anomaly diagnosis. We use datasets of different scales based on real world applications to verify the effectiveness of HG-PAD, and the experimental results show that HG-PAD achieves better diagnostic performance compared with existing baseline methods.

Encrypted tunnels are increasingly applied to privacy protection, however, a passive eavesdropper can still infer which website a user is visiting via website fingerprinting (WF). State-of-the-art WF suffers from several critical challenges in a realistic multi-tab web browsing scenario, where the number of concurrent tabs is dynamic and uncertain, training a separate model for each website is too overweight to deploy, and the robustness against the packet loss, duplication and disorder caused by dynamic network conditions is rarely considered. To address these challenges, we propose a robust and lightweight multi-tab WF method over the encrypted tunnel, named RobustWF. Due to the causality relationship between user’s request and website’s response, RobustWF employs causality correlation to associate the interactive packets belonging to the same website together, which form a causality chain. Then, RobustWF utilizes context learning to capture the dependencies between the causality chains. The missing of some specific details does not have a significant impact on the overall structure of target web, thus enhancing the robustness of RobustWF. To make the model lightweight enough, RobustWF trains an integrated model to adapt to the dynamic number of concurrent tabs. The experimental results demonstrate that the accuracy of RobustWF improves 14% in dynamic multi-tab WF scenario compared to the State-of-the-art method.

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.

Low Earth Orbit Satellite Networks (LEO-SNs) have emerged as a promising paradigm for future space information networks. However, the time-varying topology, link intermittency, limited onboard resource and relatively long transmission distance imposed unprecedented challenges on guaranteeing the delay Quality of Service (QoS). In contrast with existing routing-based or resource optimization-based solutions, onboard processing provides an alternative way to reduce the transmission delay by dwindling the transmitted data size. The employment of onboard processing makes it critical to select a routing path with sufficient energy and properly allocate resources for transmission and processing. This paper studies the untouched onboard processing aided transmission delay minimization problem of LEO-SNs. A Distributed Network State Learning (DNSL) mechanism is proposed for synchronizing the network states, which induces Potential Field (PF) to model both the attraction of resources and the repulsion of transmission load. By jointly considering the channel conditions, onboard resources and transmission load, a Deep Q-network (DQN) based Intelligent In-orbit Routing (DIIR) algorithm is proposed for selecting a routing path with good channel conditions, sufficient energy and low transmission load to facilitate onboard processing. Moreover, a Deep Deterministic Policy Gradient (DDPG) based Intelligent Resource Allocation (DIRA) algorithm is provided to achieve intelligent and continuous resource allocation for exploiting onboard processing to minimize the transmission delay, while the resource and load states of satellites on the routing path are taken into consideration by including PF as an input. Extensive simulation results demonstrate that employing onboard processing with the proposed DIIR and DIRA algorithms significantly reduces the average transmission delay and packet loss rate.

Anomaly diagnosis relying on system logs to record runtime events is essential for improving the service quality of distributed systems and reducing economic losses. However, most existing log-based anomaly detection approaches depend on the assumption of the fixed quantitative or sequential patterns of a normal log event sequence. This assumption is challenged in the context of practical distributed and parallel systems due to the dynamic pattern of the log sequence, log data noise as well as concurrency of multiple anomalies. Against these challenges, this paper aims to perform the robust log-based anomaly diagnosis by capturing the event context information of the log event graph, called LogGraph, instead of straightforwardly employing the fixed quantitative or sequential patterns of the log records, thus reducing its sensitivity to the log flaws and the concurrency of multiple anomalies. Specifically, in order to handle multiple anomalies concurrency, LogGraph invokes the association rule to decouple log sequences. It further reinterprets a log record sequence into a log event graph modeled by event semantic embedding and event adjacency matrix. An attention-based Gated Graph Neural Network (GGNN) model is developed to capture the semantic information of the log graph, which enables the fine-grained and robust anomaly identification of the proposed scheme. We use real log data sets collected from Hadoop systems and network switches to verify the effectiveness of the proposed LogGraph in log data scenarios that contain noise and multiple anomalies concurrency problems. The experimental results show that the proposed LogGraph achieves high performance and strong robustness in anomaly diagnosis.

Video delivery over wireless networks with limited network resources and dynamically changing channel quality is an important challenge, and one of the most promising solutions for tackling this problem is to employ multicast transmissions, which improves network resource utilization efficiency. This article focuses on delivering video concurrently to multiple users over mobile networks leveraging Multicast Broadcast Multimedia Service (MBMS) and Mobile Edge Computing (MEC) technology. We propose a$k$-means clustering and cooperative bargaining game-based adaptive video multicast solution (KGS) over mobile edge networks, with the goal of providing high-quality video delivery service in an envisaged MBMS service area across multiple cell sites. By taking user subgrouping, resource allocation, and bitrate adaptation into account, we establish a Cooperative Bargaining Game (CBG) based joint optimization model for multiple Multicast Broadcast Synchronized Frequency Network (MBSFN) users in mobile edge networks. Then we transform this model into a two-stage convex optimization problem and a nonlinear integer programming problem. We propose a heuristic approach to solve them and achieve a Pareto optimal video delivery strategy for all users. Finally, the efficiency of the proposed scheme is evaluated through extensive simulations.

The proliferation of 360-degree video applications has brought significant challenges to existing networks. To meet the requirements of high transmission rate, low interaction latency, and high reliability, Mobile Edge Computing (MEC) has emerged as a promising technology that enables caching and processing at network edges. In this article, we present DACOD360, a deadline-aware content delivery system for the 360-degree video streaming over MEC networks. To address the challenges such as unpredictable viewports, uneven cached tiles, concurrent requests, and dynamic bandwidth, we formulate the deadline-aware delivery problem as a long-term integer program model to maximize the Quality of Experience (QoE) under the constraints of network bandwidth, cache capacity, and deadline. This optimization problem is a complex sequential decision that considers both deadline-constrained service quality at the temporal scale and multi-user resource allocation at the spatial scale. To solve it, we decompose the original problem into two sub-problems and solve them iteratively using Deep Reinforcement Learning (DRL) and Cooperative Bargaining Game (CBG). Comprehensive experiments are conducted in a wide variety of environments, and the results demonstrate that our proposed scheme outperforms the state-of-the-art schemes in terms of long-term QoE, traffic reduction, and other metrics.

Due to the concentrated distribution of network traffic, the Internet is highly vulnerable to link flooding attack in Distributed Denial-of-Service attacks (DDoS-LFA), which utilizes the legitimate low-rate attack traffic to block the selected network area. In recent years, building trusted networks has been considered as a promising strategy to address the security issues. Nevertheless, deploying a trusted link defense mechanism in the attacked network area faces many challenges imposed by the smart scheme and legitimate disguise of DDoS-LFA. In order to overcome these challenges, we propose a blockchain-based DDoS-LFA detection and mitigation scheme, named CREDIT, to guarantee the security of attacked area, while existing works only use blockchain to share the detection results of traditional solutions. CREDIT uses blockchain to record and share the information of links and flows in real time, which enables routers in the protected area to easily trace the paths of all active flows and capture the fragile links. On the basis of link features, a credible deep learning method performed on randomly selected nodes is proposed to detect DDoS-LFA against data spoofing. When an attack alarm is raised, CREDIT performs similarity analysis to locate attackers and migrate suspicious traffic based on the flow features of alarm links. Experimental results based on real implementation and attack testbed show that, by integrating blockchain, CREDIT performs better than traditional non-blockchain-based DDoS-LFA defense methods when faced with data tampering.

As a new computing paradigm, multi-data center computing enables service providers to deploy their applications close to the users. However, due to the spatio-temporal changes in workloads, it is challenging to coordinate multiple distributed data centers to provide high-quality services while reducing service operation costs. To address this challenge, this article studies the joint optimization problem of task scheduling and resource scaling in multi-data center systems. Since the task scheduling and the resource scaling are usually performed in different timescales, we decompose the joint optimization problem into two sub-problems and propose a two-timescale optimization framework. The short-timescale task scheduling can promptly relieve the bursty arrivals of computing tasks, and the long-timescale resource scaling can adapt well to the long-term changes in workloads. To address the distributed optimization problem, we propose a two-timescale multi-agent deep reinforcement learning algorithm. In order to characterize the graph-structured states of connected data centers, we develop a directed graph convolutional network based global state representation model. The evaluation indicates that the proposed algorithm is able to reduce both the task makespan and the task timeout while maintaining a reasonable cost.

Deploying microservices on the cooperative edge computing system greatly shortens the interaction delay between users and service and alleviates the traffic burden on the backbones, which has emerged as a new paradigm for service provision. However, it is challenging to embed microservices, having diverse resource demands and heterogeneous invocation relationships, into a distributed edge computing system with irregular network topology. In order to characterize the invocation relationship, we conceive a graph attention network based model to capture the structural features of microservices. Similarly, we propose a multi-channel directed graph convolutional network model to capture the spatial dynamic of edge resources distribution, which jointly considers the heterogeneity of the edge nodes and the links between them. Then, we develop a sequence-to-sequence based multi-step decision model, which maps the feature sequence of the current state to a sequence of deployment actions. Using this model, we further propose a microservice deployment algorithm based on graph neural network aided deep reinforcement learning, where a parallel asynchronous training process is used to accelerate convergence. The performance evaluation shows that the proposed algorithm can improve the deployment success ratio and resource utilization, while ensuring the load balance of edge nodes.

The rapid growth of mobile network traffic poses major challenges for current wireless networks regarding bandwidth, delay, mobility, and stability. To overcome these obstacles, a new network architecture called Information-Centric Networking (ICN) has emerged, effectively addressing these issues and enhancing content delivery efficiency. However, with the in-network content cache, anyone including unauthorized users can access the content from intermediate network nodes. In response to this challenge, this paper proposes an efficient and lightweight ICN content access control framework based on a hybrid-coding mechanism that combines two or more encoding operations, which does not impose additional complexity on ICN routers. In the proposed scheme, the content is first divided into multiple original blocks, and these original blocks are encoded into encoded blocks using hybrid-coding operations. Each authorized user can obtain private decoding information from the content provider, and decode them into original content using its private decoding information. The proposed scheme can fully utilize ICN’s in-network cache capability and defend against a wide range of attacks. Furthermore, security analysis, ndnSIM-based simulation, and real-world experiments demonstrate the scheme’s security, performance, and scalability.

The IPv6 over low-power wireless personal area network (6LoWPAN) protocol stack is a promising solution to connect wireless sensor networks (WSNs) with the Internet for realizing a ubiquitous network interconnection of all things. However, 6LoWPAN networks face a critical challenge to control congestion caused by the burst of data traffic from wireless sensors. Packet loss will occur when the buffer overflows. This article focuses on the loss-tolerant congestion control problem in 6LoWPAN networks, which has not been addressed in existing works. We formulate the congestion control problem as a noncooperative Markov game framework and conceive a novel congestion control method, namely, deep reinforcement learning-aided loss-tolerant congestion control (DLCC), to alleviate congestion while maintaining a tolerable packet loss imposed by the buffer overflow. The proposed DLCC employs deep reinforcement learning (DRL) to solve the curse of state dimensionality, while packet loss constraints are handled by utilizing Lagrange multipliers to integrate the reward with loss constraints. By dynamically updating Lagrange multipliers in an online learning procedure, DLCC finds the optimal congestion control policy. Our simulation results show that DLCC maintains the packet loss rate below the tolerable threshold in the presence of congestion. In contrast to existing hybrid congestion control algorithms, the proposed DLCC algorithm is more energy efficient and provides higher throughput, lower average delay, and better fairness.

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.

The Tucker Decomposition (TKD) is able to provide the low-dimensional and informative representations of real-world large-scale tensorial data, which are necessary to extract potential features and enhance the original data. However, computing such decomposition directly for a dense tensor is usually computationally elusive, due to the repetitive operations of computing large-scale tensor-matrix product. Instead of direct decomposition, this paper proposes an efficient algorithm for seeking the Faster TKD of the large-scale tensor, which is a lightweight decomposition approach based on the technique of randomized sampling. The proposed algorithm first converts the original large-scale tensor into a small-scale subtensor via full-mode sampling operation, and then the core tensor of TKD can be computed directly based on the subtensor with low complexity. Finally, an approximate TKD of the original large-scale tensor can be obtained after sequentially computing approximate full-mode factor matrices. A theoretical error analysis is provided to show that the approximation error approximates zero with high probability, and the proposed algorithm is verified based on real tensorial data of$\text{23821.24}~GB$.

Driven by the prevalence of the computation-intensive and delay-intensive mobile applications, Mobile Edge Computing (MEC) is emerging as a promising solution. Traditional task offloading methods usually rely on centralized decision making, which inevitably involves a high computational complexity and a large state space. However, the MEC is a typical distributed system, where the edge servers are geographically separated, and independently perform the computing tasks. This fact inspires us to conceive a distributed cooperative task offloading system, where each edge server makes its own decision on how to allocate local computing resources and how to migrate tasks among the edge servers. To characterize diverse task requirements, we divide the arrival tasks into different priorities according to the tolerance time, which enables to dynamically schedule the local computing resources for reducing the task timeout. In order to coordinate the independent decision makings of geographically separate edge servers, we propose a priority driven cooperative task offloading algorithm based on multi-agent deep reinforcement learning, where the decision making of each edge server not only depends on its own state but also on the shared global information. We further develop a Variational Recurrent Neural Network (VRNN) based global state sharing model which significantly reduces the communication overhead among edge servers. The performance evaluation conducted on a movement trajectories dataset of mobile devices verifies that the proposed algorithm can reduce the task consumption time and improve the edge computing resources utilization.

Aeronautical Ad-hoc Networks (AANETs) have been proposed as the promising complement to terrestrial networks for promoting the global interconnection to provide in-flight network service, emergency communication, vessel traffic service, etc. However, the large network scale of AANETs may induce severe synchronization overhead when the traditional topology-based networking algorithms are adopted. Moreover, the high-dynamic topology and changeable flight routes make the existing position-based routing algorithms suffer loop routing and forwarding failure. Motivated by these problems, this paper aims at developing efficient and low-cost networking and routing algorithms relying on the concept of dynamic virtual topology which organizes the disordered topology of AANETs into a structural and simplified one. The basic idea is that each connected aircraft is assigned with a unique and sequentially increased Virtual Identifier (VID) and thus all the connected aircrafts are organized into a virtual cluster consisting of one trunk and several branches. An event-driven synchronization mechanism is leveraged for maintaining the virtual topology as well as relieving the communication burden imposed by periodical broadcasting. By jointly considering the geographic locations and the virtual locations of aircrafts, we formulate the routing problem in AANETs as a weighted distance minimization problem, and further propose a novel routing algorithm, namely Trunk-Branch Cooperation aided Routing (TBCR) algorithm. Specifically, TBCR employs the geographic greedy forwarding strategy for enhancing its flexibility and boosts the routing efficiency by adopting the loop-free virtual topology based local forwarding. For extending the networking and routing algorithms to the global range, a multi-domain routing solution is also provided. Extensive experimental results show that the proposed Virtual Topology based Networking (VTN) cooperated with TBCR can reduce at least 30% average end-to-end transmission delay in large-scale AANETs and provide more than 90% lower synchronization overhead than the existing solutions.

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.

Caching contents close to end-users can improve the network performance, while causing the problem of guaranteeing consistency. Specifically, solutions are classified into validation and invalidation, the latter of which can provide strong cache consistency strictly required in some scenarios. To date, little work on the analysis of cache invalidation has been covered. In this work, by using conditional probability to characterize the interactive relationship between existence and validity, we develop an analytical model that evaluates the performance (hit probability and server load) of four different invalidation schemes with LRU replacement under arbitrary invalidation frequency distribution. The model allows us to theoretically identify some key parameters that affect our metrics of interest and gain some common insights on parameter settings to balance the performance of cache invalidation. Compared with other cache invalidation models, our model can achieve higher accuracy in predicting the cache hit probability. We also conduct extensive simulations that demonstrate the achievable performance of our model.

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.

The soaring mobile data traffic demands have spawned the innovative concept of mobile edge caching in ultra-dense next-generation networks, which mitigates their heavy traffic burden. We conceive cooperative content sharing between base stations (BSs) for improving the exploitation of the limited storage of a single edge cache. We formulate the cooperative caching problem as a partially observable Markov decision process (POMDP) based multi-agent decision problem, which jointly optimizes the costs of fetching contents from the local BS, from the nearby BSs and from the remote servers. To solve this problem, we devise a multi-agent actor-critic framework, where a communication module is introduced to extract and share the variability of the actions and observations of all BSs. To beneficially exploit the spatio-temporal differences of the content popularity, we harness a variational recurrent neural network (VRNN) for estimating the time-variant popularity distribution in each BS. Based on multi-agent deep reinforcement learning, we conceive a cooperative edge caching algorithm where the BSs operate cooperatively, since the distributed decision making of each agent depends on both the local and the global states. Our experiments conducted within a large scale cellular network having numerous BSs reveal that the proposed algorithm relying on the collaboration of BSs substantially improves the benefits of edge caches.

This paper considers the anti-jamming optimization problem for tracking multiple moving target flight vehicles in the presence of deception jammers. Since the radar is not able to separate the real target vehicles from a large number of deceptive vehicles, we promote the existing non-anti-jamming tracking model to the anti-jamming partially observable Markov decision process-based (POMDP-based) game tracking model by establishing a new anti-jamming Bayesian tracker. The proposed tracker is able to separate the hidden real target vehicles and establish their accurate trajectories, but the limited radar resources will decrease the accuracy. In order to effectively utilize the limited resources to guarantee the anti-jamming performance, this work deduces the anti-jamming performance gradients with respect to the resource management policy, which can be estimated with the asymptotically vanished biases. With the gradient estimates, the optimal anti-jamming resource management policy can be found with the tolerable complexity. The convergence analysis shows that the algorithm converges to a Nash equilibrium solution with probability 1. Numerical results show that the proposed algorithm can obtain the accurate target trajectories in the presence of jammers.

Promptly discovering unknown network attacks is critical for reducing the risk of major loss imposed on organizations and information infrastructure. This paper aims at developing an intelligent intrusion detection system capable of classifying known attacks as well as inferring unknown ones. To achieve this, we formulate the problem of fine-grained known/unknown intrusion detection as a two-stage minimization problem, where the first stage is to seek a score measure for minimizing the empirical risk of misclassifying the known attacks, while the second stage is to find another score measure for minimizing the identification risk of inferring unknown attacks. The hierarchical nature of problem formulation allows us to employ the class conditioned auto-encoders to construct a hierarchical intrusion detection framework. Since the reconstruction errors of unknown attacks are generally higher than that of the known attacks, we further employ extreme value theory in the second stage to model the distribution of reconstruction errors for differentiating known/unknown attack. To further reduce the false positive rate, we add a benign clustering module for learning the multimodal distribution of benign traffic. We conduct an experiment on two widely used datasets for assessing intrusion detection. The results show that the proposed method improves the detection rate of unknown attacks while keeping a low false positive rate.

Current schemes for Dynamic Adaptive Streaming over HTTP (DASH) are mainly client-driven. Thus, in the scenario of multiple users watching the same video, repeated subscription and data transmission results in an under-utilization of network bandwidth resources. Additionally, competition for limited network resources of individual users may motivate selfish behaviors, which leads to unfairness and sub-optimal utility of video services. In this paper, Multimedia Broadcast Multicast Service (MBMS) in mobile edge networks for multi-bitrate video sessions is applied to overcome these limitations. We formulate a non-linear integer programming (NLIP) model, which jointly optimize bitrate adaptation and resource allocation for multiple users. This model takes video quality, playback interruptions, and quality oscillations as linear constraints to maximize multicast users' Quality of Experience (QoE). Due to NP-Hardness of this problem, we propose a heuristic greedy algorithm, which can work out the optimal or near-optimal solution with low time complexity. The evaluation results demonstrate that our method can achieve Pareto Optimality of the system utility, and maximize users' QoE while ensuring fairness.

With the development of Virtual Reality (VR) technology, the growing number of VR users puts tremendous pressure on network bandwidth. The tile-based scheme is proposed to reduce the transmission size of 360° video and improve bandwidth utilization. However, when the Field of View (FoV) of the user changes unexpectedly, the tile-based scheme will cause video distortion and quality switching by unacceptable delay. Therefore, many methods are proposed to cache the tiles that users are most likely to playback in Cloud/Edge to decrease delay. However, the dynamic adaptive bitrate delivery and the caching decision is a complex joint optimization problem, which will be a dimensional explosion problem when the scale of users and videos is large. In this paper, we design a QoE-based 360° video adaptive bitrate delivery and caching scheme aiming to maximize the quality of experience (QoE) of multi-user and ensure the fairness of users. To solve this optimization problem which is proved to be NP-Hard, we propose a bitrate selection and caching decision algorithm by greedy strategy. Numerical simulation results demonstrate that our algorithm significantly improves cache hit rate and QoE performance compared with other algorithms with fairness guaranteed.

As a promising candidate for future Internet architecture, Named Data Networking (NDN) can achieve significant potential advantages over current TCP/IP based Internet in content distribution and mobility support, etc. However, the communication mode in NDN that one Interest packet for pulling one data packet is likely to incur Interest packets flooding and to cause extremely large-scale Pending Interest Table (PIT) of the NDN router, which may substantially degrade the performance of NDN. Moreover, the absence of predefined connections also induces another challenge for NDN to manage the successive and concurrent requests from consumers efficiently. In this paper, we propose flow-based NDN (f-NDN) architecture capable of supporting flow transmission mode for addressing the aforementioned challenges. In the context of f-NDN, a data flow is defined as an aggregate of data packets with the same name prefix, and a flow Interest packet is introduced to pull a data flow rather than a single data packet. The PIT, CS, and FIB are re-designed to enable f-NDN to operate at the granularity of flows. Bitmap structure aided error handling mechanism is further presented for f-NDN to deal with the flow transmission's uncertain failures. The built-in flow support in f-NDN allows us to conceive a flow-level multi-path transmission regime for balancing the traffic in NDN network and reducing the time consumed for pulling the entire content. Weight-based flow Interest splitting algorithm and optimal rate control algorithm are both proposed for optimizing multi-path transmission. We illustrate the capability of the proposed architecture in supporting flow transmission and multipath by implementing it in both simulator and prototype systems. The evaluation results are also presented to show its achievable performance.

Named Data Networking (NDN) is an emerging future network architecture that changes the network communication model from push mode to pull mode, which leads to the requirement of a new mechanism of congestion control. To fully exploit the capability of NDN, a suitable congestion control scheme must consider the characteristics of NDN, such as connectionless, in-network caching, content perceptibility, etc. In this paper, firstly, we redefine the congestion control objective for NDN, which considers requirements diversities for different contents. Then we design and develop an efficient congestion control mechanism based on deep reinforcement learning (DRL), namely DRL-based Congestion Control Protocol (DRL-CCP). DRL-CCP enables consumers to automatically learn the optimal congestion control policy from historical congestion control experience. Finally, a real-world test platform with some typical congestion control algorithms for NDN is implemented, and a series of comparative experiments are performed on this platform to verify the performance of DRL-CCP.

Both wireless small-cell communications and software-defined networking (SDN) in wired systems continue to evolve rapidly, aiming for improving the quality of experience (QoE) of users. Against this emerging landscape, we conceive scalable video streaming over SDN-aided dense smell-cell networks by jointly optimizing the video layer selection, the wireless resource allocation, and the dynamic routing of video streams. In the light of this ambitious objective, we conceive a dense software-defined small-cell network architecture for the fine-grained manipulation of the video streams relying on the cooperation of small-cell base stations. Based on this framework, we formulate the scalable video streaming problem as maximizing the time-averaged QoE subject to a specific time-averaged rate constraint as well as to a resource constraint. By employing the classic Lyapunov optimization method, the problem is further decomposed into the twin sub-problems of video layer selection and wireless resource allocation. Via solving these sub-problems, we derive a video layer selection strategy and a wireless resource allocation algorithm. Furthermore, we propose a beneficial routing policy for scalable video streams with the aid of the so-called segment routing technique in the context of SDN, which additionally exploits the collaboration of small-cell base stations. Our results demonstrate compelling performance improvements compared with the classic PID control theory-based method.

Counting on-road vehicles in the highway is fundamental for intelligent transportation management. This paper presents the first highway vehicle counting method in compressed domain, aiming at achieving comparable estimation performance with the pixel-domain methods. Counting in compressed domain is rather challenging due to limited information about vehicles and large variance in vehicle numbers. To address this problem, we develop new low-level features to mitigate the challenge from insufficient information in compressed videos. The new proposed features can be easily extracted from the coding-related metadata. Then, we propose a hierarchical classification-based regression (HCR) model to estimate the number of vehicles from the compressed-domain low-level features for individual frame. HCR hierarchically divides the traffic scenes into different cases according to the density of vehicles such that the large variance of traffic scenes can be effectively captured. Beside the spatial regression in each frame, we propose a locally temporal regression model to further refine the counting results, which exploits the continuous variation characteristics of the traffic flow. We extensively evaluate the proposed method on real highway surveillance videos. The experimental results consistently show that the proposed method is very competitive compared with the pixel-domain methods, which can reach similar performance with much lower computational cost.

Explosive growth in video traffic volumes incurs a high percentage of redundancy in today's Internet, following the 80–20 rule. Fortunately, the advanced in-network cache is considered as an effective scheme for eliminating the repetitive traffic by caching the popular content in network nodes. Besides, the emerging software-defined networking (SDN) enables centralized control and management, as well as the collaboration between network devices and upper applications. Moreover, the Network Functions Virtualization is also developed to support for customized network functions, including caching and streaming. This inspires us to design an SDN-assisted multimedia streaming Video-on-Demand system, integrating in-network cache, to improve the quality of service. The designed architecture is capable of reducing the redundant traffic via the reusable duplications. In particular, it can achieve greater performance gains by deploying specific scheduling policy. We further propose a variable-length interval cache strategy for RTP streaming, which can realize the self-adaptive adjustment of the size of cached video segments based on their access patterns. Our goal is to efficiently utilize the limited storage resources and increase the cache hit ratio. We present the theoretical analysis to demonstrate the attainable performance of the proposed algorithm; furthermore, the integrated system design is implemented as a prototype to show its feasibility and applicability. Ultimately, emulation experiments are conducted to evaluate the achievable performance improvement more comprehensively.

Scalable video streaming over femtocell networks relying on two-tier spectrum-sharing is designed for coping with time-varying channel conditions, stringent video QoS requirements as well as with strong cross-tier interference between the over-sailing macro- and the femtocells. Dynamic video layer selection and resource allocation are invoked to enable the adaptation of the scalable video streaming service to the dynamics of both channel quality and interference price fluctuations. We formulate the design as a constrained stochastic optimization problem, which strikes a compelling compromise between the perceivable quality of experience and the monetary implications of the interference. Since the time scale of resource allocation is more short term than that of the video layer selection, we decompose the original long-term utility optimization problem into a pair of readily tractable subproblems with the aid of two different time-scales by invoking the powerful technique of Lyapunov drift and optimization. By exploiting the specific structure of these subproblems, low-complexity algorithms are derived for dynamic video layer selection and resource allocation, which rely on the near-instantaneously available information rather than on any prior statistical knowledge. Finally, we derive the analytical bounds of the theoretically achievable performance. Experimental results are presented for characterizing the performance attained.

Scalable video coding is a promising technique to enable flexible video transmission for heterogeneous terminals and varying channel throughput. However, it is challenging to perform in-network adaptation in conventional networks because the network nodes are uncontrollable and transparent for media streaming applications. Software-defined networking (SDN) is an attractive network technology that supports the applications to collaborate with network nodes to achieve intelligent and dynamic service provisioning. Against this changing network landscape, we redesign the scalable multimedia multicast streaming by exploiting the complete network knowledge of the SDN controller to enable intelligent scalable video transmission. The proposed scalable multimedia multicast streaming framework is capable of in-network identifying, processing, and manipulating the media streams. In order to achieve the in-network adaptation, we apply equivalent bandwidth theory to estimate the affordable video layers that a link may accommodate, and apply finite-state machine to implement adaptive enhancement layer switching for multicast paths. In contrast to IP multicast, the proposed method is a controllable multicast scheme, which provides admission control in a multicast context, in-network adaptation, and supporting heterogeneous devices having different display capability. We further implement a prototype for illustrating the success of the proposed solution. The experimental results are also presented to show the effectiveness of the proposed equivalent bandwidth based adaptive enhancement layer switching algorithm.

This paper considers adaptive transmission of Scalable Video Coding (SVC) stream over a fading channel in Energy Harvesting (EH) aided communication systems. The aim is to enable the transmitter having the ability of adaptively adjusting the number of video layers to be transmitted based on the available energy. Since the depletion of the energy in the battery impacts the interruption of the video transmission, the energy starvation probability is defined for equivalently characterizing a Quality-of-Experience (QoE) metric. We formulate the problem of EH-aided video transmission as maximizing the number of video layers for transmission while keeping the energy starvation probability below a threshold. Classic large deviation theory is applied to estimate the energy starvation probability from online measurements. The simulation results verify that the algorithm proposed have the adaptation capability to accommodate both the energy-dynamics and the channel-dynamics for improving the video quality subject to an unobjectionable level of transmission interruption rate.

This paper solves the joint problem of admission control and routing for the video transmission in software-defined networking (SDN). We utilize next generation network called SDN technology to construct an architecture for the proposed combined optimization problem. Our heuristic algorithm of the proposed combined optimization problem can be deployed on this architecture. Based on this background, we formulate the combined optimization problem into Markov Decision Process (MDP) with the aim of maximizing the average reward. In allusion to the challenge of the curses of dimensionality, an online learning framework is designed by employing neuro-dynamic programming (NDP) method. We construct an emulation platform based on POX controller and Mininet to confirm high efficiency of our solution. Experiment results show that our NDP based algorithm has an observably performance improvement compared with OSPF based benchmark algorithm.

Aiming at improving the energy efficiency of the primary battery in hybrid energy supply wireless communication system, this paper proposes an enhanced date transmission control strategy with the save-then- transmit protocol, where the energy supply of the transmitter comes from both the primary battery and the energy harvester. We formulate the problem of data management as a probabilistic constrained optimization problem, i.e., minimizing the energy delivered from the primary battery while keeping the overflow probability of the data buffer below a desired threshold which can be defined as Quality of Service(QoS) constraint. We use the historical burst packet arrival of data and data buffer limit to estimate the overflow probability by large deviation principle, and an online control strategy is derived. The major contribution of this paper is that the proposed strategy guarantees a stable and high quality of energy supply in energy harvesting (EH) wireless communication system. Numerical results are presented to validate the effectiveness of the proposed control strategy.

This paper considers the optimization problem of joint admission control and routing for the video streaming service in wired software-defined networking (SDN). With the aid of the network operating system, SDN is able to support the dynamic nature of future network functions and intelligent applications. Against this changing network landscape, we rely on FlowVisor-based virtualization in the context of OpenFlow-based wired SDN to design an open optimization architecture for the joint admission control and routing, which supports flexible and agile deployment of advanced joint admission control and routing strategies. Following this architecture, we interpret the joint admission control and routing problem into the Markov decision process for maximizing the overall “revenue.” In order to solve the issue of the curses of dimensionality, we invoke the function approximation technique in the context of approximate dynamic programming to conceive an online learning framework. By applying kernel-based autonomous feature extraction into the function approximation, we develop an approximate dynamic programming-based joint admission control and routing for video streaming service, which is apt to be implemented in the proposed open architecture. An emulation platform based on FlowVisor, POX, and Mininet is constructed for demonstrating the success of the proposed solution. The experimental results are presented to show the performance improvement of the proposed scheme by comparing it with the Q-learning algorithm and open shortest path first-based benchmark scheme.

In an IaaS cloud, virtual machines (VMs), also called instances, may be classified as reserved instances and on-demand instances. The reserved instances having long-term commitments and one-time payment are appropriate for the steady or predictable workloads, while for short-term, spiky or unpredictable workloads, the on-demand instances having flexible hourly payment and no long-term commitments may be more suitable for reducing the cost. In this paper, we consider the economical provisioning of reserved and/or on-demand instances for meeting time-varying computing workload of compute-intensive applications. In order to achieve this, we conceive a strategy for determining the amount of the purchased instances dynamically in order to minimize the total computing cost while keeping quality-of-service (QoS). By mapping QoS as the overload probability, we propose a dynamic instance provisioning strategy based on the large deviation principle, which is capable of calculating the minimum number of instances for the upcoming demands subject to the overload probability below a desired threshold. In addition, a reserved instance provisioning strategy for further reducing the total cost is also proposed by applying the autoregressive (AR) model to calculate the number of reserved instances for the average computation requirements. Finally, the simulations are performed based on real workload traces to show the attainable performance of the proposed instance provisioning strategy for the computing service in an IaaS cloud.

Current typical video conferencing connection is bridged by a multipoint control unit (MCU), which may cause large delay and communication bottleneck for the whole system. With the development of network technology, a video conferencing system can be implemented based on software-defined networking (SDN), which makes the service controllable and improves the scalability and flexibility. Additionally, a video encoding method called scalable video coding (SVC) can also help. In this paper, we propose a video conferencing architecture based on SDN-enabled SVC multicasting, which discards the traditional Internet group management protocol (IGMP) and MCU. The system implements SVC multicast streaming to satisfy different device capabilities of various conference terminals. The SDN controller is responsible for dynamically managing and controlling the layers of a video stream when a conference member faces network congestion. Also, a conference manager is designed to facilitate the management of the conference members. Experimental results show that our system can not only provide a flexible and controllable video delivery, but also reduce the network usage while guaranteeing the quality of service (QoS) of video conferencing.

Scalable Video Coding (SVC) has been raised as a promising technique to enable flexible video transmission for mobile users with heterogeneous terminals and varying channel capacities. In this paper, we design an adaptive layer switching algorithm for on-demand scalable video service based on receiver's buffer underflow probability (BUP). Since the low quality of channel may lead to a low buffer fullness, the buffer fullness is an indicator for reflecting the channel condition and we define BUP for characterizing the mismatch between the video bitrate and the channel throughput. Accordingly, the adaptive SVC transmission problem is formulated as the adaptive adjustment of video layers based on BUP. This allows us to optimize the attainable video quality, while keeping BUP below a desired level. To estimate BUP, we derive an analytical model based on the large deviation principles. Then, an online layer switching algorithm is proposed using this estimation model, which is capable of accommodating different channel qualities without any prior knowledge of the channel variations and of the video characteristics. We further introduce a perturbation-based layer switching approach for reducing the quality fluctuating issue caused by frequent layer switches, thus improving the viewer's quality of experience. A system prototype is implemented to evaluate the success of the proposed method. We also conduct simulations in multiuser scenarios with real video traces and the results demonstrate that the proposed algorithm is capable of improving the playback experience, while keeping a low playback interruption rate and quality variation.

In this paper, we consider the adaptive transmission problem of scalable video in an energy harvesting communication system. The stochastic nature of the harvested energy puts a new challenge on the video transmission. Against this challenge, we formulate the adaptive scalable video transmission problem as maximizing the time average quality of the transmitted video subject to the energy constraint for reducing the playback interruption and the video quality smoothness constraint. In order to solve this problem, the Lyapunov optimization method is applied to derive an online dynamic layer transmission algorithm (DLTA). The simulation results show that the proposed DLTA can achieve better performance in terms of the received video quality and the convergence rate than a conventional reinforcement learning algorithm like the Q-learning method. It is also illustrated that the energy and smoothness constraints are beneficial for controlling the behavior of DLTA.

In video streaming service, multicast mode is a promising way to complement unicast delivery of content, since it deliveries the video content to a broad range of receivers. It is considered as an efficient scheme to reduce redundant traffic in the networks. In this paper, we propose an OpenFlow based architecture for implementing Scalable Video Coding (SVC) multicast streaming. It enables in-network identifying, processing and manipulating the media streams, which makes prompt bitrate adaptation possible in response to network fluctuations. The heterogeneous video quality demand from the heterogeneous device also can be satisfied by customizing the multicast traffic through a centralized OpenFlow controller. We implement a testbed following the proposed architecture in our campus. With OpenFlow, we deploy IP multicast in a new way without Internet Group Management Protocol (IGMP) or any multicast addresses. Experiments implemented on the testbed show that our approach can provide a flexible and controllable video multicast streaming service and improve the usage of bandwidth resource in the condition of guaranteeing multicast receivers' Quality of Experience (QoE).

Smart grid is an emerging technology which is designed to integrate advanced communication technologies into electrical power grids. In smart grid, automatic communications between machine devices is necessary. The number of Machine-Type Communication (MTC) devices will increase exponentially as smart grid technologies are further developed and deployed. It is a critical issue to deal with the massive accesses from an enormous number of MTC devices while guarantee the desired quality of service (QoS). In this paper, we formulate this problem as a queuing problem. Then we propose an adaptive massive access management by applying a probability estimation measurement, which is based on large deviation theory. The results demonstrate that our algorithm can adaptively adjust allocation rate and provide a better QoS. The results also show that our algorithm can improve the spectral efficiency.

Generalised eigendecomposition problem for a symmetric matrix pencil is reinterpreted as an unconstrained minimisation problem with a weighted non‐linear criterion. The analytical results show that the proposed criterion has a unique global minimum which corresponds to the principal generalised eigenvectors, thus guaranteeing the global convergence via iterative methods to search the minimum. A gradient‐based adaptive algorithm and a fixed point iteration‐based adaptive algorithm are derived for the generalised eigendecomposition, which both work in parallel and avoid the error propagation effect of sequential‐type algorithms. By applying the stochastic approximation theory, the global convergence of the proposed adaptive algorithm is proved. The performance of the proposed method is evaluated by simulations in terms of convergence rate, estimation accuracy as well as tracking capability.

We consider adaptively extracting multiple principal generalized eigenvectors, which can be widely applied in modern signal processing. By using the deflation technique, the problem is reformulated into an unconstrained minimization problem. An adaptive sequential algorithm based on the Newton method is proposed to solve this problem. To improve its real-time performance, a parallel version of this algorithm is provided on the basis of certain approximation. Furthermore, a two-layer neural network is constructed to execute the adaptive algorithm. The asymptotic convergence of this algorithm is rigorously proved by stochastic approximation theory. The simulation results demonstrate the effectiveness of the proposed algorithms.

A receiver-driven adaptive layer switching algorithm is proposed for adapting the video bitrate to match the achievable network throughput. It relies on a QoS-constrained equivalent bandwidth estimator employed at the receiver, which is used for triggering the adjustment of video layers at the video source. Simulations are conducted to illustrate its efficiency by showing that it is capable of accommodating different channel qualities without their prior knowledge.

In this paper, we propose an adaptive scalable layer selection policy for video streaming over wireless networks by jointly considering the wireless channel conditions and scalable video coding. Our goal is to design a scheme which could provide both long-term smooth and high play-back quality for each user, while maintaining the fairness among multi-users. We first design an adaptive long-term layer selection scheme which determines the number of layers each user would receive in order to maximize the total video quality of all streams while avoiding high variation in the number of video layers. We then employ a scheduling algorithm for slot allocation considering both current channel capacity and deadline. We compare our proposed policies with some reference schemes and simulation results show the effectiveness of our proposed scheme.

This paper considers the problem of dynamic cluster reconfiguration for computation intensive services. In order to provide a quality-of-service in terms of overload probability, we formulate the problem of energy consumption as a constrained optimization problem, i.e., minimizing the number of active servers to reduce the energy consumption while keeping the overload probability below a desired threshold. An overload probability estimation model is derived by applying large deviation principle, and an online measurement based algorithm is developed to decide the number of servers to power on/off, which makes decision based on current workload without any prior knowledge of the workload statistics. Moreover, the proposed dynamic cluster reconfiguration algorithm iteratively adjusts the number of the active servers, instead of directly determining the number of active servers that is hard to guarantee optimality for the nonstationary workloads. Since the distribution of the workloads among the servers has an impact on potential active servers to turn off, a server scheduling strategy is proposed to collaborate with the proposed decision algorithm to achieve better energy conservation. In order to provide an integrated solution, we present an event model-based implementation to demonstrate the practical application of the proposed approach. Finally, we evaluate the performance of the scheme by using real workloads. The experimental results show the adaptability of the proposed approach to the variations in the workload and robustness of quality-of-service for nonstationary workloads.

Adaptive media playout (AMP) control is proposed in order to compensate for the bit-rate fluctuation of networks, which may result in playout interruptions in video streaming application. Most AMP algorithms found in the literature trigger playout-rate adjustments based on the buffer fullness or its variation. However, the challenge of the threshold based methods is to select the appropriate threshold for triggering a playout-rate adjustment owing to the unknown fluctuation of the channel quality and the video bitrate. We conceive an adaptive media playout regime based on underflow probability estimation, which requires no significant statistical knowledge of the previous tele-traffic load. To achieve this, we present an underflow probability estimation model based on large deviation theory relying on the buffer fullness and on its variation. We will then directly use the underflow probability to trigger the actions of playout control, instead of using indirect methods based on a buffer fullness threshold or buffer fullness variation threshold. Experiments based on MPEG-4 Variable Bit-Rate encoded video and VBR channels associated with Adaptive Modulation and Coding are conducted in order to investigate the achievable performance of the proposed algorithm. Our simulation results demonstrate an improved performance in comparison to other recent AMP algorithms.

In this brief, we consider extracting generalized eigenvectors in parallel for the generalized eigendecomposition problem. The problem is formulated as an optimization problem of minimizing an unconstrained quartic cost function based on the weighted rule. It is shown that the proposed weighted cost function has a unique global minimum, which corresponds to the principal generalized eigenvectors. In order to estimate the principal generalized eigenvector matrix efficiently, we simplify the quartic cost function as a quadric one by making an appropriate approximation, and then derive a fast algorithm for extracting the principal generalized eigenvector in parallel. We also show the application of the proposed algorithm in blind source separation. Numerical simulations are performed, and the results demonstrate the performance of the proposed algorithm.

Video-on-demand has been emerging as a very popular internet service in recent years. But energy consumption is becoming a critical issue as these services scale up. In this paper, we propose an energy-aware server provisioning strategy which dynamically turns on/off servers in order to adaptively tailor active servers to dynamic user load. We initiate a stochastic model which characterizes unique properties such as bandwidth and power consumption of video-on-demand systems. We then employ a measurement-based adaptive online user load predictor and apply large deviation theory to our model to develop global strategy. Simulation confirms that our strategy can lead a significant amount of energy savings with little or no user experience degradation.

A class of resource location service for distributed VoD system, which combines one-hop k-random walk and global centralized indexing service, is studied. First, in order to minimizing the cost of communication and guaranteeing the response time performance, a Markov model is proposed to describe the queue phenomenon, admission control and the process of location. In this model, control is related with not only states but also events, which introduce more information as the control basis. Then, an optimization algorithm that combines policy gradient estimation and stochastic approximation is proposed. This algorithm can deal with constraints and depend on no system parameter. Finally, an illustrative simulation is performed to demonstrate the effectiveness of model and algorithm.

In this letter, we study the problem of adaptive retrieval of multiple sinusoids in white noise. The frequency estimation problem can be reformulated as an unconstrained optimization problem. From the proposed unconstrained cost function, a numerically robust and low complexity modified Newton algorithm is derived for tracking frequencies. A rigorous convergence analysis of the proposed algorithm by adopting Ljung's ordinary differential equation approach is presented. Simulation results show that the proposed adaptive frequency estimation algorithm has fast convergence and excellent tracking capability in nonstationary environment.