IEEE Transactions on Network Science and Engineering最新文献_第8页

Fluid/Movable Antenna Meets Active RIS: A Next Generation ISAC System Design 流体/可移动天线满足主动RIS：新一代ISAC系统设计

IF 7.3 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-01 Epub Date: 2025-10-28 DOI: 10.1109/TNSE.2025.3626520

Jiaju Zhu;Haisu Wu;Hong Ren;Cunhua Pan;Rui Shi

引用次数: 0

Unlinkable and Decentralized Credential Management for Agentic AI 代理AI的不可链接和分散凭证管理

IF 7.3 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-01 Epub Date: 2026-03-19 DOI: 10.1109/TNSE.2026.3695095

Qi Liu;Peng Jiang;Jun Zhu;Shaolong Tang;Wei Wang;Liehuang Zhu

{"title":"Unlinkable and Decentralized Credential Management for Agentic AI","authors":"Qi Liu;Peng Jiang;Jun Zhu;Shaolong Tang;Wei Wang;Liehuang Zhu","doi":"10.1109/TNSE.2026.3695095","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3695095","url":null,"abstract":"The evolution toward agentic AI is transforming edge devices from passive sensors into autonomous agents capable of collaborative decision-making. However, the open interaction among these agents exposes the system to malicious intrusions, making robust authentication essential to validate legitimate agents. Traditional authentication mechanisms still suffer from sensitive information leakage due to the potential exposure of agents' real-time locations and session linkability. Furthermore, previous privacy-preserving techniques often employ a centralized manager to prevent information leakage, whose compromise breaks the security of the whole system. In this paper, we propose B-CM, a blockchain-based credential management mechanism for agentic AI. B-CM augments traditional authentication by integrating anonymous credentials with blockchain technology to enable unlinkable and auditable agent authentication in a decentralized manner. To optimize efficiency, we leverage off-chain computation performed by edge servers and fast on-chain verification, accompanied by credential aggregation. We implement B-CM over Hyperledger Fabric to evaluate its performance. Experimental results show that the off-chain computation remains highly efficient, requiring only 8.35 s for distributed key generation and 9.7 ms for credential issuance, while the on-chain computational overhead is nearly negligible.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"9379-9393"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148508034","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}

引用次数: 0

Quantum Deep Deterministic Policy Gradient for Digital Twin-Enabled Semantic IoV Networks 数字双支持语义车联网的量子深度确定性策略梯度

IF 7.3 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-01 Epub Date: 2025-06-18 DOI: 10.1109/TNSE.2025.3580705

James Adu Ansere;Sasinda C. Prabhashana;Nidhi Simmons;Octavia A. Dobre;Hyundong Shin;Trung Q. Duong

引用次数: 0

CentralityPool: Centrality-Aware Hierarchical Graph Pooling CentralityPool：中心性感知分层图池

IF 7.3 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-01 Epub Date: 2025-06-25 DOI: 10.1109/TNSE.2025.3583119

Mohammadreza Ghanbari;Saeed Asadi Bagloee;Jianzhong Qi;Majid Sarvi

引用次数: 0

Secure Distributed Estimation-Based Data-Driven Leader-Following Control for Discrete-Time MASs Under Deception Attacks and Sensor Faults 欺骗攻击和传感器故障下的离散时间质量安全分布式估计数据驱动先导跟随控制

IF 7.3 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-01 Epub Date: 2025-08-22 DOI: 10.1109/TNSE.2025.3601851

Qiangyuan Ren;Guangdeng Chen;Xiao-Jie Peng;Hongyi Li;Tingwen Huang

{"title":"Secure Distributed Estimation-Based Data-Driven Leader-Following Control for Discrete-Time MASs Under Deception Attacks and Sensor Faults","authors":"Qiangyuan Ren;Guangdeng Chen;Xiao-Jie Peng;Hongyi Li;Tingwen Huang","doi":"10.1109/TNSE.2025.3601851","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3601851","url":null,"abstract":"In this paper, we consider the leader-following consensus problem for a class of heterogeneous nonlinear discrete-time multi-agent systems (MASs) with unknown dynamics under network deception attacks and sensor faults. The deception attacks considered occur in the communication network between the leader and the followers, compromising the authenticity of the leader's information received by the followers and affecting the cooperative performance. Since not all followers can directly access the leader's information and their measurements may be influenced by deception attacks and sensor faults, a secure distributed estimation algorithm is proposed to allow each follower to obtain accurate information about the leader. To achieve indirect estimation of unknown fault signals, nonlinear autoregressive with exogenous input neural network (NARXNN)-based fault estimators are proposed, combining the open-loop and closed-loop modes of NARXNN. Moreover, considering that followers have unknown dynamics and exhibit heterogeneity, decentralized model-free adaptive controllers are designed based on the estimation of leader information and fault signals. Unlike methods that use consensus error for indirect consensus control, the proposed approach allows each agent to directly acquire a leader estimate and achieve direct leader-following consensus control using only local input/output data. Finally, simulation examples confirm the effectiveness of the proposed data-driven leader-following consensus control method.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"1754-1769"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509523","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}

引用次数: 0

A Federated Blockchain Security for MEC-Enabled IoT Networks in Industrial 5.0 工业5.0中支持mec的物联网网络的联合区块链安全性

IF 7.3 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-01 Epub Date: 2025-12-09 DOI: 10.1109/TNSE.2025.3640426

Eric Gyamfi;James Adu Ansere;Mohsin Kamal;Mian Ahmad Jan;Houbing Song;Muhammad Tariq

{"title":"A Federated Blockchain Security for MEC-Enabled IoT Networks in Industrial 5.0","authors":"Eric Gyamfi;James Adu Ansere;Mohsin Kamal;Mian Ahmad Jan;Houbing Song;Muhammad Tariq","doi":"10.1109/TNSE.2025.3640426","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3640426","url":null,"abstract":"Secure communication is vital for the Industrial Internet of Things (IIoT) in the fifth revolution. However, resource constraints of most IIoT devices prevent the use of conventional security models. Multi-access mobile edge computing (MEC) offers a solution by bringing computational resources closer to the network edge, but centralized security solutions limit scalability and flexibility in MEC-enabled IIoT systems. Additionally, the distributed nature of MEC servers poses data privacy and security challenges. This paper introduces a novel network security approach for IIoT using federated blockchain (FB) and a machine learning-based verification system (ML). MEC optimizes the FB model, ensuring data integrity and confidentiality between the IIoT’s local network cluster and external devices. Public key cryptography secures data shared within the local network cluster, and the ML-based verification model validates IIoT devices requesting encryption key-pair updates and joining the MEC’s FB. This integrated approach surpasses conventional security solutions by enhancing scalability, data privacy, and adaptability to the evolving IIoT network topology. The methodology is implemented and evaluated using a realistic IoT testbed, demonstrating improved network security while maintaining MEC-enabled IIoT system performance and scalability.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5312-5323"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509539","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}

引用次数: 0

Teleportation Links: Mitigating Catastrophic Forgetting in Decentralized Federated Learning 传送链接：减轻分散联邦学习中的灾难性遗忘

IF 7.3 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-01 Epub Date: 2025-09-17 DOI: 10.1109/TNSE.2025.3611109

Xu Wang;Yuanzhu Chen;Qiang Ye;Octavia A. Dobre

{"title":"Teleportation Links: Mitigating Catastrophic Forgetting in Decentralized Federated Learning","authors":"Xu Wang;Yuanzhu Chen;Qiang Ye;Octavia A. Dobre","doi":"10.1109/TNSE.2025.3611109","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3611109","url":null,"abstract":"Decentralized approaches are inspired by the self-organizing principles observed in natural and social systems. These methods offer a scalable and resilient framework for collaborative learning. Decentralized federated learning (DFL) uses these principles to avoid the dependency on a central controller. However, a key challenge arises from data heterogeneity. This often leads to catastrophic forgetting, where the model significantly loses its ability to remember and use previously learned information. This loss of knowledge can reduce the reliability of DFL in critical applications, such as autonomous systems, financial services, energy management, and transportation networks. To tackle these challenges, in this paper, we investigate how data distribution affects DFL performance, with a focus on how bias propagates across nodes. We also explore how local model learning rates affect the trade-off between learning stability and convergence speed. At the same time, we evaluate the performance drops at individual nodes. Furthermore, to improve connectivity and speed up knowledge sharing, we propose adding a limited number of teleportation links, which aims to reduce the average distance between pairs of nodes. Extensive experimental results demonstrate the effectiveness of this strategy, showing reduced catastrophic forgetting, faster convergence, and improved resilience across various scenarios.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"2167-2180"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509644","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}

引用次数: 0

Event-Triggered Secure Formation Tracking of Delayed Multi-UAV Systems Subject to DoS Attacks DoS攻击下延迟多无人机系统的事件触发安全编队跟踪

IF 7.3 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-01 Epub Date: 2025-07-07 DOI: 10.1109/TNSE.2025.3586304

Chen Liu;Lei Liu;Jinde Cao;Yiping Luo

引用次数: 0

Non-Cooperative Multi-Agent Reinforcement Learning Exploiting Population Dynamics 利用种群动态的非合作多智能体强化学习

IF 7.3 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-01 Epub Date: 2025-07-07 DOI: 10.1109/TNSE.2025.3586602

Junling Li;Hao Zhang;Shuqi Ke;Jianwei Huang;Nan Chen;Xuemin Shen

{"title":"Non-Cooperative Multi-Agent Reinforcement Learning Exploiting Population Dynamics","authors":"Junling Li;Hao Zhang;Shuqi Ke;Jianwei Huang;Nan Chen;Xuemin Shen","doi":"10.1109/TNSE.2025.3586602","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3586602","url":null,"abstract":"Non-cooperative multi-agent reinforcement learning (MARL) faces significant challenges due to non-stationarity and non-unique learning goals. While equilibrium-based analysis frameworks effectively address these challenges, existing approaches suffer from high computational complexity as the number of agents increases. To overcome this limitation, we propose a population game-based Q-learning (Pop-Q) algorithm that computes Nash equilibrium (NE) policies through efficient population dynamics. Our approach represents population evolution using ordinary differential equations (ODEs) and introduces two key mechanisms to reduce the complexity of solving these ODEs. By adjusting the number of iterations in population dynamics, our algorithm enables a controllable trade-off between computational complexity and equilibrium accuracy. Experimental results demonstrate that Pop-Q achieves competitive performance in two-agent settings and superior performance in three-agent environments compared to existing equilibrium-based MARL algorithms. The proposed algorithm has significant potential applications in modern systems requiring decentralized coordination, including intelligent traffic systems, warehouse automation, and autonomous aerial vehicle (AAV) swarm-aided communication networks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"684-700"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148509858","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}

引用次数: 0

Near-Optimal Reinforcement Learning With Shuffle Differential Privacy 随机差分隐私的近最优强化学习

IF 7.3 2区计算机科学

IEEE Transactions on Network Science and Engineering Pub Date : 2026-01-01 Epub Date: 2026-03-19 DOI: 10.1109/TNSE.2026.3695143

Shaojie Bai;Mohammad Sadegh Talebi;Chengcheng Zhao;Peng Cheng;Jiming Chen

{"title":"Near-Optimal Reinforcement Learning With Shuffle Differential Privacy","authors":"Shaojie Bai;Mohammad Sadegh Talebi;Chengcheng Zhao;Peng Cheng;Jiming Chen","doi":"10.1109/TNSE.2026.3695143","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3695143","url":null,"abstract":"Reinforcement learning (RL) is a powerful tool for sequential decision-making, but its application is often hindered by privacy concerns arising from its sensitive interaction data. This challenge is particularly acute in advanced networked systems, where learning from operational and user data can expose systems to privacy inference attacks. Existing differential privacy (DP) models for RL are often inadequate: the centralized model requires a fully trusted server, creating a single point of failure risk, while the local model incurs significant performance degradation that renders it unsuitable for many networked applications. This paper addresses this gap by leveraging the emerging shuffle model of privacy, an intermediate trust model that provides strong privacy guarantees without a centralized trust assumption. We present Shuffle Differentially Private Policy Elimination (SDP-PE), the first generic policy elimination-based algorithm for episodic RL under the shuffle model. Our method introduces a novel exponential batching schedule and a “forgetting” mechanism to balance the competing demands of privacy and learning performance. Our analysis shows that SDP-PE achieves a near-optimal regret bound, demonstrating a superior privacy-regret trade-off with utility comparable to the centralized model while significantly outperforming the local model. The numerical experiments also corroborate our theoretical results and demonstrate the effectiveness of SDP-PE. This work establishes the viability of the shuffle model for secure data-driven decision-making in networked systems.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"9807-9826"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510515","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}

引用次数: 0