After natural disasters, such as earthquakes or tsunamis, terrestrial communication networks often become inoperative due to infrastructure collapse. Simultaneously, damage to roads and transportation systems inevitably isolates different parts of the affected area, making it challenging for emergency vehicles to reach critical locations and deploy mobile Base Stations (BSs). In such scenarios, UnmannedAerial Vehicles (UAVs) serve as a flexible and efficient solution. With the capability to establish temporary communication links, UAVs can provide emergency coverage for ground entities. In this paper, we propose a Dynamic Priority-based UAV-assisted Vehicular Ad-hoc Network (VANET) Routing (DPUVR) protocol for post-disaster message transmission. Specifically, DPUVR is a trajectory-based method for controlling the direction of message forwarding. DPUVR utilizes a multi-attribute decision-making method to adaptively evaluate the message delivery capability of candidate nodes (in this paper, nodes refer to both UAVs and vehicles), taking into account trajectory similarity, surplus energy, link survival time, remaining distance cost and queuing delay. In addition, we propose a dynamic prioritization delivery model. It evaluates the priority of messages in node buffers, selects appropriate candidate nodes and then chooses the best relay for message forwarding to trigger timely and efficient message delivery. Extensive simulation results show that DPUVR significantly outperforms other baseline methods in terms of delivery ratio, overhead, average delivery latency and average buffering time.
{"title":"A Novel UAV-Assisted VANET Routing Protocol for Post-Disaster Emergency Communications","authors":"Zhijie Fan;Mansi Zhang;Yue Cao;Zilong Liu;Omprakash Kaiwartya;Yasir Javed;Faisal Bashir Hussain","doi":"10.1109/TNSE.2025.3644432","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3644432","url":null,"abstract":"After natural disasters, such as earthquakes or tsunamis, terrestrial communication networks often become inoperative due to infrastructure collapse. Simultaneously, damage to roads and transportation systems inevitably isolates different parts of the affected area, making it challenging for emergency vehicles to reach critical locations and deploy mobile Base Stations (BSs). In such scenarios, UnmannedAerial Vehicles (UAVs) serve as a flexible and efficient solution. With the capability to establish temporary communication links, UAVs can provide emergency coverage for ground entities. In this paper, we propose a Dynamic Priority-based UAV-assisted Vehicular Ad-hoc Network (VANET) Routing (DPUVR) protocol for post-disaster message transmission. Specifically, DPUVR is a trajectory-based method for controlling the direction of message forwarding. DPUVR utilizes a multi-attribute decision-making method to adaptively evaluate the message delivery capability of candidate nodes (in this paper, nodes refer to both UAVs and vehicles), taking into account trajectory similarity, surplus energy, link survival time, remaining distance cost and queuing delay. In addition, we propose a dynamic prioritization delivery model. It evaluates the priority of messages in node buffers, selects appropriate candidate nodes and then chooses the best relay for message forwarding to trigger timely and efficient message delivery. Extensive simulation results show that DPUVR significantly outperforms other baseline methods in terms of delivery ratio, overhead, average delivery latency and average buffering time.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"4863-4882"},"PeriodicalIF":7.9,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1109/TNSE.2025.3644438
Ziqi Chen;Jun Du;Chunxiao Jiang;Xiangwang Hou;Zhu Han;H. Vincent Poor
With the rapid development of the low-altitude economy, privacy protection has become a significant challenge in the unmanned aerial vehicles (UAV) networks. Federated learning (FL) provides a concrete framework for addressing privacy concerns in the low-altitude networks by enabling training without exposing raw data. However, there remains a risk of data leakage during aggregation of parameter updates from local models in the FL framework. Existing approaches have introduced differential privacy (DP) to mitigate this issue, but adding DP noise can degrade the performance of the training process. To further enhance the efficiency and accuracy of model training, we propose a novel framework based on DP and adaptive sparsity for FL, named DP-FedAS. On the one hand, this framework reduces communication and training overhead through an adaptive sparsity module. On the other hand, it mitigates privacy errors caused by DP noise by reducing the noise introduced during global aggregation via sparsity, thereby alleviating the performance degradation. Furthermore, we provide detailed theoretical proofs for the convergence of the proposed algorithm and the privacy guarantees it offers. Simulation results validate that DP-FedAS improves global model accuracy by 20%, and reduces communication cost by 23%, while maintaining a robust level of privacy protection. The proposed framework strikes an optimal balance among communication efficiency, privacy preservation, and model performance.
{"title":"Differential Privacy-Based Adaptive Sparse Federated Learning in UAV Networks","authors":"Ziqi Chen;Jun Du;Chunxiao Jiang;Xiangwang Hou;Zhu Han;H. Vincent Poor","doi":"10.1109/TNSE.2025.3644438","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3644438","url":null,"abstract":"With the rapid development of the low-altitude economy, privacy protection has become a significant challenge in the unmanned aerial vehicles (UAV) networks. Federated learning (FL) provides a concrete framework for addressing privacy concerns in the low-altitude networks by enabling training without exposing raw data. However, there remains a risk of data leakage during aggregation of parameter updates from local models in the FL framework. Existing approaches have introduced differential privacy (DP) to mitigate this issue, but adding DP noise can degrade the performance of the training process. To further enhance the efficiency and accuracy of model training, we propose a novel framework based on DP and adaptive sparsity for FL, named DP-FedAS. On the one hand, this framework reduces communication and training overhead through an adaptive sparsity module. On the other hand, it mitigates privacy errors caused by DP noise by reducing the noise introduced during global aggregation via sparsity, thereby alleviating the performance degradation. Furthermore, we provide detailed theoretical proofs for the convergence of the proposed algorithm and the privacy guarantees it offers. Simulation results validate that DP-FedAS improves global model accuracy by 20%, and reduces communication cost by 23%, while maintaining a robust level of privacy protection. The proposed framework strikes an optimal balance among communication efficiency, privacy preservation, and model performance.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5128-5144"},"PeriodicalIF":7.9,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145929437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1109/TNSE.2025.3644385
Zhen Yin;Wei Wang;Yanyu Cheng;Yiliang Liu;Xiaozhen Lu
The emerging increasingly sophisticated, intelligent, and stealthy network attacks pose more severe security threats to the edge network. In particular, the emergence of novel intelligent attacks makes it a challenging issue to obtain sufficient attack samples, and thus classical deep learning-driven intrusion detection frameworks (IDSs) become ineffective. To tackle this issue, we introduce a novel intrusion detection framework leveraging few-shot class-incremental learning (FSCIL) capabilities to achieve robust detection of emerging threats with few samples. This approach pre-trains a backbone traffic classification model and employs few-shot training with prototypical networks. To further reduce catastrophic forgetting while improving both accuracy and system robustness, we incrementally fine-tune the classification model with supervised contrastive learning, and also realize rapid adaptation to new attacks. Evaluations on the intrusion detection datasets CIC-IDS2017 and USTC-TF2016 demonstrate that our framework consistently outperforms baseline models for emerging attacks detection with few attack samples while preserving effective recognition of known threats.
{"title":"Efficient Intrusion Detection for Edge Network via Multi-Stage Few-Shot Class-Incremental Learning","authors":"Zhen Yin;Wei Wang;Yanyu Cheng;Yiliang Liu;Xiaozhen Lu","doi":"10.1109/TNSE.2025.3644385","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3644385","url":null,"abstract":"The emerging increasingly sophisticated, intelligent, and stealthy network attacks pose more severe security threats to the edge network. In particular, the emergence of novel intelligent attacks makes it a challenging issue to obtain sufficient attack samples, and thus classical deep learning-driven intrusion detection frameworks (IDSs) become ineffective. To tackle this issue, we introduce a novel intrusion detection framework leveraging few-shot class-incremental learning (FSCIL) capabilities to achieve robust detection of emerging threats with few samples. This approach pre-trains a backbone traffic classification model and employs few-shot training with prototypical networks. To further reduce catastrophic forgetting while improving both accuracy and system robustness, we incrementally fine-tune the classification model with supervised contrastive learning, and also realize rapid adaptation to new attacks. Evaluations on the intrusion detection datasets CIC-IDS2017 and USTC-TF2016 demonstrate that our framework consistently outperforms baseline models for emerging attacks detection with few attack samples while preserving effective recognition of known threats.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"4689-4706"},"PeriodicalIF":7.9,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-15DOI: 10.1109/TNSE.2025.3643634
Kun Yan;Wenping Ma;Shaohui Sun
The 6G Computing Power Network (CPN) is envisioned to orchestrate vast, distributed computing resources for future intelligent applications. However, achieving efficient, trusted, and privacy-preserving computing resource sharing in this decentralized environment poses significant challenges. To address these intertwined issues, this article proposes a holistic blockchain and evolutionary algorithm-based computing resource sharing (BECS) mechanism. BECS is designed to dynamically and adaptively balance task offloading among computing resources within the 6G CPN, thereby enhancing resource utilization. We model computing resource sharing as a multi-objective optimization problem, aiming to navigate these trade-offs. To tackle this NP-hard problem, we devise a kernel-distance-based dominance relation and incorporate it into the Non-dominated Sorting Genetic Algorithm III (NSGA-III), thereby significantly enhancing population diversity. In addition, we propose a pseudonym scheme based on zero-knowledge proofs to protect user privacy during computing resource sharing. Finally, security analysis and simulation results demonstrate that BECS can effectively leverage all computing resources in the 6G CPN, thereby significantly improving resource utilization while preserving user privacy.
{"title":"BECS: A Privacy-Preserving Computing Resource Sharing Mechanism for 6G Computing Power Network","authors":"Kun Yan;Wenping Ma;Shaohui Sun","doi":"10.1109/TNSE.2025.3643634","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3643634","url":null,"abstract":"The 6G Computing Power Network (CPN) is envisioned to orchestrate vast, distributed computing resources for future intelligent applications. However, achieving efficient, trusted, and privacy-preserving computing resource sharing in this decentralized environment poses significant challenges. To address these intertwined issues, this article proposes a holistic blockchain and evolutionary algorithm-based computing resource sharing (BECS) mechanism. BECS is designed to dynamically and adaptively balance task offloading among computing resources within the 6G CPN, thereby enhancing resource utilization. We model computing resource sharing as a multi-objective optimization problem, aiming to navigate these trade-offs. To tackle this NP-hard problem, we devise a kernel-distance-based dominance relation and incorporate it into the Non-dominated Sorting Genetic Algorithm III (NSGA-III), thereby significantly enhancing population diversity. In addition, we propose a pseudonym scheme based on zero-knowledge proofs to protect user privacy during computing resource sharing. Finally, security analysis and simulation results demonstrate that BECS can effectively leverage all computing resources in the 6G CPN, thereby significantly improving resource utilization while preserving user privacy.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"4725-4742"},"PeriodicalIF":7.9,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Structural node embedding is a fundamental technique for encoding the topology of a graph into low-dimensional vectors. However, many existing methods generate position-dependent embeddings, meaning that structurally similar nodes are represented dissimilarly merely due to their distance in the graph. Furthermore, these approaches often lack interpretability and robustness against structural noise. To address these challenges, this paper introduces GraphQWalk, an interpretable, unsupervised, and position-independent method that leverages the continuous quantum walk to capture structural features. Inspired by quantum physics, GraphQWalk first computes initial node features from the average transition probabilities of a particle in a continuous quantum walk. These features, encoding multi-scale structural information, are then aggregated within multi-hop neighborhoods to incorporate local context. Extensive experiments demonstrate that GraphQWalk effectively captures diverse structural roles, achieving superior robustness and performance over baseline models in downstream tasks from classification to cross-graph alignment.
{"title":"GraphQWalk: Learning Structural Node Embeddings via Continuous Quantum Walk","authors":"Guojun Liu;Juanhong Zhao;Houzhou Wei;Zhengxiong Zhou;Yunfei Song;Xiaomei Zhou;Guangzhi Qi","doi":"10.1109/TNSE.2025.3644803","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3644803","url":null,"abstract":"Structural node embedding is a fundamental technique for encoding the topology of a graph into low-dimensional vectors. However, many existing methods generate position-dependent embeddings, meaning that structurally similar nodes are represented dissimilarly merely due to their distance in the graph. Furthermore, these approaches often lack interpretability and robustness against structural noise. To address these challenges, this paper introduces GraphQWalk, an interpretable, unsupervised, and position-independent method that leverages the continuous quantum walk to capture structural features. Inspired by quantum physics, GraphQWalk first computes initial node features from the average transition probabilities of a particle in a continuous quantum walk. These features, encoding multi-scale structural information, are then aggregated within multi-hop neighborhoods to incorporate local context. Extensive experiments demonstrate that GraphQWalk effectively captures diverse structural roles, achieving superior robustness and performance over baseline models in downstream tasks from classification to cross-graph alignment.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"4779-4796"},"PeriodicalIF":7.9,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145886555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-11DOI: 10.1109/TNSE.2025.3642623
Siyu Qian;Xiaolin Wang;Fangfei Li;Yi'ang Ren
Edge computing enables real-time estimation for mission-critical Industrial Internet of Things (IIoT) systems by processing massive data streams at the network edge. Existing methods struggle to balance estimation performance and energy efficiency under varying reliability and latency requirements. We investigate Multi-Link Operation (MLO) to improve adaptability under dynamic wireless conditions. However, existing schemes offer limited flexibility in transmission modes and rely on simplified network models, which restricts the potential for MLO-specific performance optimization. To overcome these challenges, we propose a novel flexible transmission mode selection mechanism called Synchronous-Asynchronous Co-existence Multi-Link Aggregation (SACMLA). Based on switchable MLO modes, we formulate a scheduling-estimation co-design problem that optimizes the trade-off between estimation error covariance and transmission energy consumption. Given the environmental complexity and the action space explosion caused by the joint optimization of transmission mode, packet-to-link mapping, and energy allocation, we design a Proximal Policy Optimization (PPO)-based Hierarchical Deep Reinforcement Learning for Multi-Link Co-optimization (HDRL-MLC), a two-tier architecture where a PPO-based outer loop dynamically adjusts scheduling, and an inner loop optimizes energy allocation in MLO. Simulation results demonstrate the necessity of the SACMLA mechanism and highlight the superior performance of the PPO-based HDRL-MLC algorithm over flat algorithms in balancing multiple communication metrics and adapting to varying traffic conditions.
{"title":"Multi-Link Enabled Reliable and Low Latency Transmission Design for Edge Estimation","authors":"Siyu Qian;Xiaolin Wang;Fangfei Li;Yi'ang Ren","doi":"10.1109/TNSE.2025.3642623","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3642623","url":null,"abstract":"Edge computing enables real-time estimation for mission-critical Industrial Internet of Things (IIoT) systems by processing massive data streams at the network edge. Existing methods struggle to balance estimation performance and energy efficiency under varying reliability and latency requirements. We investigate Multi-Link Operation (MLO) to improve adaptability under dynamic wireless conditions. However, existing schemes offer limited flexibility in transmission modes and rely on simplified network models, which restricts the potential for MLO-specific performance optimization. To overcome these challenges, we propose a novel flexible transmission mode selection mechanism called Synchronous-Asynchronous Co-existence Multi-Link Aggregation (SACMLA). Based on switchable MLO modes, we formulate a scheduling-estimation co-design problem that optimizes the trade-off between estimation error covariance and transmission energy consumption. Given the environmental complexity and the action space explosion caused by the joint optimization of transmission mode, packet-to-link mapping, and energy allocation, we design a Proximal Policy Optimization (PPO)-based Hierarchical Deep Reinforcement Learning for Multi-Link Co-optimization (HDRL-MLC), a two-tier architecture where a PPO-based outer loop dynamically adjusts scheduling, and an inner loop optimizes energy allocation in MLO. Simulation results demonstrate the necessity of the SACMLA mechanism and highlight the superior performance of the PPO-based HDRL-MLC algorithm over flat algorithms in balancing multiple communication metrics and adapting to varying traffic conditions.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"4707-4724"},"PeriodicalIF":7.9,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1109/TNSE.2025.3641579
Ye Wang;Jingjing Wang;Jianrui Chen;Xiangwang Hou;Ziyang Wang;Chunxiao Jiang
The evolution of the Internet of Vehicles (IoV) has introduced computation-intensive and latency-sensitive applications that challenge traditional cloud architectures. Although drone-aided IoV offers a flexible solution, it presents a complex optimization problem. The core challenge lies in balancing task offloading efficiency with crucial operational safety constraints, such as collision avoidance and battery management, a gap often overlooked in existing research. This paper addresses this problem by first modeling the drone-aided task offloading system as a constrained multi-agent Markov decision process. Based on this framework, we propose a novel safe multi-agent reinforcement learning algorithm (MARL) named Lagrangian-constrained multi-agent policy optimization (LC-MAPO). The LC-MAPO integrates safety constraints into the twin delayed deep deterministic policy gradient (TD3) actor-critic framework using Lagrangian duality theory. The algorithm's effectiveness was validated in three distinct simulation scenarios and compared against an unconstrained multi-agent deep deterministic policy gradient (MADDPG) algorithm and a greedy algorithm. Experimental results demonstrate that LC-MAPO achieves superior performance in both safety adherence and task processing efficiency.
{"title":"Drone-Aided Secure Task Offloading Optimization for Internet of Vehicles: Review, Challenges and Method","authors":"Ye Wang;Jingjing Wang;Jianrui Chen;Xiangwang Hou;Ziyang Wang;Chunxiao Jiang","doi":"10.1109/TNSE.2025.3641579","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3641579","url":null,"abstract":"The evolution of the Internet of Vehicles (IoV) has introduced computation-intensive and latency-sensitive applications that challenge traditional cloud architectures. Although drone-aided IoV offers a flexible solution, it presents a complex optimization problem. The core challenge lies in balancing task offloading efficiency with crucial operational safety constraints, such as collision avoidance and battery management, a gap often overlooked in existing research. This paper addresses this problem by first modeling the drone-aided task offloading system as a constrained multi-agent Markov decision process. Based on this framework, we propose a novel safe multi-agent reinforcement learning algorithm (MARL) named Lagrangian-constrained multi-agent policy optimization (LC-MAPO). The LC-MAPO integrates safety constraints into the twin delayed deep deterministic policy gradient (TD3) actor-critic framework using Lagrangian duality theory. The algorithm's effectiveness was validated in three distinct simulation scenarios and compared against an unconstrained multi-agent deep deterministic policy gradient (MADDPG) algorithm and a greedy algorithm. Experimental results demonstrate that LC-MAPO achieves superior performance in both safety adherence and task processing efficiency.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"4596-4615"},"PeriodicalIF":7.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1109/TNSE.2025.3642184
Yixuan Lv;Lei Liu;Yan-Jun Liu;Yang Chen
In this article, based on nodes and edges, we research the fully distributed generalized Nash equilibrium (GNE) search problem for aggregate games under coupling constraints with bounded disturbances. In order to solve this problem, the node adaptive control law and edge adaptive control law are introdued. In the node-based adaptive control law, agents dynamically adjust their weights through parameters tuning, whereas the edge-based adaptive control law introduces additional adjustable parameters to enable the multi-agent system to autonomously regulate the gain size. Compared with the aggregation game discussed earlier, it can make the agent adjust its behavior under the condition of complete information. Then, it is proved that the GNE of the system can remain asymptotically stable under these two strategies. Although preliminary explorations of disturbances mitigation in GNE problems exist in the literature, the dynamic compensation mechanisms and system performance control under bounded disturbances in aggregation games with coupling constraints remain insufficiently explored. In this article, the disturbances compensations are designed to minimize the damage of the system by keeping the system stable when bounded disturbances occur in the aggregate game under coupling constraints. Finally, the real effectiveness of the designed strategy is proved by simulation experiments.
{"title":"Fully Distributed Nash Equilibrium Search for Aggregate Games Under Coupling Constraints With Bounded Disturbances","authors":"Yixuan Lv;Lei Liu;Yan-Jun Liu;Yang Chen","doi":"10.1109/TNSE.2025.3642184","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3642184","url":null,"abstract":"In this article, based on nodes and edges, we research the fully distributed generalized Nash equilibrium (GNE) search problem for aggregate games under coupling constraints with bounded disturbances. In order to solve this problem, the node adaptive control law and edge adaptive control law are introdued. In the node-based adaptive control law, agents dynamically adjust their weights through parameters tuning, whereas the edge-based adaptive control law introduces additional adjustable parameters to enable the multi-agent system to autonomously regulate the gain size. Compared with the aggregation game discussed earlier, it can make the agent adjust its behavior under the condition of complete information. Then, it is proved that the GNE of the system can remain asymptotically stable under these two strategies. Although preliminary explorations of disturbances mitigation in GNE problems exist in the literature, the dynamic compensation mechanisms and system performance control under bounded disturbances in aggregation games with coupling constraints remain insufficiently explored. In this article, the disturbances compensations are designed to minimize the damage of the system by keeping the system stable when bounded disturbances occur in the aggregate game under coupling constraints. Finally, the real effectiveness of the designed strategy is proved by simulation experiments.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"4651-4668"},"PeriodicalIF":7.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a cell-free massive multiple-input multiple-output (CF-mMIMO) system coexisting with device-to-device (D2D) pairs is investigated under ultra-dense wireless networks (UDWNs), aiming to accommodate the growing number of devices and escalating data traffic demands in future sixth-generation (6G) networks. To overcome the challenge of pilot shortage in UDWNs, non-orthogonal multiple-access (NOMA) and successive interference cancellation (SIC) technologies are employed, enabling users within the same cluster to share pilots efficiently. A closed-form expression for the spectral efficiency of conjugate beamforming receivers is derived, revealing that pilot contamination caused by pilot sharing severely degrades system performance. A novel hypergraph coloring (HGC)-based pilot assignment algorithm is proposed to address this issue, effectively capturing the complex cumulative interference among multiple users and D2D pairs. The algorithm constructs users’ interference hypergraphs using a user-centric access point (AP) selection strategy and allocates pilots based on interference weights within these hyperedges. Numerical results demonstrate that the proposed scheme significantly improves spectral efficiency, offering a promising solution for interference management in CF-mMIMO systems under UDWNs.
{"title":"Efficient Pilot Assignment for D2D-Underlaid NOMA Cell-Free Massive MIMO Systems: A Hypergraph Coloring Approach","authors":"Qin Wang;Haotian Chang;Yongxu Zhu;Mei Chen;Li Gao;Wenchao Xia;Haitao Zhao","doi":"10.1109/TNSE.2025.3640946","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3640946","url":null,"abstract":"In this paper, a cell-free massive multiple-input multiple-output (CF-mMIMO) system coexisting with device-to-device (D2D) pairs is investigated under ultra-dense wireless networks (UDWNs), aiming to accommodate the growing number of devices and escalating data traffic demands in future sixth-generation (6G) networks. To overcome the challenge of pilot shortage in UDWNs, non-orthogonal multiple-access (NOMA) and successive interference cancellation (SIC) technologies are employed, enabling users within the same cluster to share pilots efficiently. A closed-form expression for the spectral efficiency of conjugate beamforming receivers is derived, revealing that pilot contamination caused by pilot sharing severely degrades system performance. A novel hypergraph coloring (HGC)-based pilot assignment algorithm is proposed to address this issue, effectively capturing the complex cumulative interference among multiple users and D2D pairs. The algorithm constructs users’ interference hypergraphs using a user-centric access point (AP) selection strategy and allocates pilots based on interference weights within these hyperedges. Numerical results demonstrate that the proposed scheme significantly improves spectral efficiency, offering a promising solution for interference management in CF-mMIMO systems under UDWNs.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"4636-4650"},"PeriodicalIF":7.9,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830799","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the proliferation of Internet of Things (IoT) devices, IoT systems face increasingly severe threats from large-scale cyberattacks during network communications. To effectively recognize potential network threats and ensure the security of IoT, network traffic anomaly detection has been widely studied. Due to the high cost of manual labeling in real-world scenarios, only limited network traffic data is explicitly labeled as abnormal or normal. However, most existing anomaly detection methods struggle to enhance detection accuracy with minimal supervision and perform ineffectively at identifying potential unknown anomalies in unlabeled data. To address these limitations, this paper proposes WADE, a Weakly supervised Anomaly DEtection method based on Deep Reinforcement Learning (DRL). WADE enables simultaneous identification of known and unknown anomaly traffic with limited labeled data. Specifically, it incorporates a Dueling Q-network architecture and introduces a novel reward optimization mechanism that: 1) strengthens feature extraction from unlabeled data and 2) elevates decision accuracy via dynamic reward adaptation. Extensive experiments on IoT traffic datasets demonstrate that WADE outperforms four peer weakly supervised methods, achieving performance improvements of 12.2$%$ in Area Under the Precision-Recall Curve (AUC-PR) and 6.6$%$ in Area Under the Receiver Operating Characteristic Curve (AUC-ROC), which validate the effectiveness of WADE in safeguarding IoT systems.
{"title":"DRL-Based Weakly Supervised Traffic Anomaly Detection for IoT Networks","authors":"Ziteng Wang;Yiheng Ruan;Xianjun Deng;Xiaoxuan Fan;Shenghao Liu;Wei Feng;Deng Zhang","doi":"10.1109/TNSE.2025.3640175","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3640175","url":null,"abstract":"With the proliferation of Internet of Things (IoT) devices, IoT systems face increasingly severe threats from large-scale cyberattacks during network communications. To effectively recognize potential network threats and ensure the security of IoT, network traffic anomaly detection has been widely studied. Due to the high cost of manual labeling in real-world scenarios, only limited network traffic data is explicitly labeled as abnormal or normal. However, most existing anomaly detection methods struggle to enhance detection accuracy with minimal supervision and perform ineffectively at identifying potential unknown anomalies in unlabeled data. To address these limitations, this paper proposes WADE, a <italic>Weakly supervised Anomaly DEtection</i> method based on Deep Reinforcement Learning (DRL). WADE enables simultaneous identification of known and unknown anomaly traffic with limited labeled data. Specifically, it incorporates a Dueling Q-network architecture and introduces a novel reward optimization mechanism that: 1) strengthens feature extraction from unlabeled data and 2) elevates decision accuracy via dynamic reward adaptation. Extensive experiments on IoT traffic datasets demonstrate that WADE outperforms four peer weakly supervised methods, achieving performance improvements of 12.2<inline-formula><tex-math>$%$</tex-math></inline-formula> in Area Under the Precision-Recall Curve (AUC-PR) and 6.6<inline-formula><tex-math>$%$</tex-math></inline-formula> in Area Under the Receiver Operating Characteristic Curve (AUC-ROC), which validate the effectiveness of WADE in safeguarding IoT systems.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"4562-4577"},"PeriodicalIF":7.9,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145879968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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