IEEE Transactions on Network Science and Engineering最新文献

{"title":"Radio Frequency-Enhanced Multi-Factor IoT Device Authentication via Swarm Learning","authors":"Fanqin Zhou;Lei Zhang;Zhixiang Yang;Lei Feng","doi":"10.1109/TNSE.2025.3548813","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3548813","url":null,"abstract":"With the increased popularity of Internet of things (IoT) devices, security issues have notably risen in recent times. Typically, wireless IoT applications are vulnerable to impersonation attacks by malicious entities. This paper proposes a lightweight multi-factor authentication mechanism boosted by radio frequency fingerprinting (RFF) to physically identify IoT devices. A novel application of swarm learning (SL) is utilized to develop the authentication model and enable distributed authentication. This approach maintains privacy and is resilient against faults when processing RFF data from various sources. The device-side multi-factor authentication is lightweight and has been validated through a formal security model. Experimental results indicate that the proposed scheme achieves the highest authentication success rate and the lowest computational cost on the device side compared to other authentication methods, which also validated its effectiveness in defending against impersonation and poisoning attacks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2487-2499"},"PeriodicalIF":6.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492476","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}

{"title":"PCA: Semi-Supervised Segmentation With Patch Confidence Adversarial Training","authors":"Zhenghua Xu;Runhe Yang;Zihang Xu;Shuo Zhang;Yuchen Yang;Weipeng Liu;Weichao Xu;Junyang Chen;Thomas Lukasiewicz;Victor C. M. Leung","doi":"10.1109/TNSE.2025.3548416","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3548416","url":null,"abstract":"Deep-learning-based semi-supervised learning (SSL) methods have achieved a strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preferences. In this article, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. The PCA method's discriminator penalizes patch-level structures, guiding the generator to optimize different patch areas, by leveraging pixel context, the generator is driven to focus on high-frequency features, making it harder to deceive the discriminator and easy to converge to an ideal state, which more effectively guides the segmenter to generate high-quality pseudo-labels. Furthermore, at the discriminator's input, we supplement image information constraints, making it simpler to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2473-2486"},"PeriodicalIF":6.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492255","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}

{"title":"Reliable Routing and Scheduling in Time Sensitive Networks Based on Reinforcement Learning","authors":"Hao Cheng;Lei Yang;Qingfeng Zhang;Weiping Zhu","doi":"10.1109/TNSE.2025.3546100","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3546100","url":null,"abstract":"Time Sensitive Network (TSN) provides strict low latency and bounded jitter requirements for applications such as industrial systems, autonomous driving, etc. One of the important problems in TSN is to achieve high reliability and low latency by effectively routing and scheduling time-sensitive data flows. Existing work applies heuristic or integer programming to address flow routing and scheduling, yet often fail to achieve optimal solutions quickly. In this paper, we propose a new Reinforcement Learning (RL) based approach for routing and scheduling of redundant data flows, aiming to achieve load balancing on the network links as well as meeting the reliability and delay constraints. Our approach first leverages a simple heuristic algorithm to decide the redundant path candidate set, and then incorporates Proximal Policy Optimization (PPO) method to choose the most suitable multi-routing flows from the candidates, which can be aware of the network status dynamically to reduce the load on the bottleneck link of the network. On this basis, we further retrain the RL model by fine-tuning to adapt to the online environment. The simulation results show that our proposed solution outperforms the benchmark algorithms in terms of the degree of network balance by 38.7% in offline network environments and in terms of average delay by 14.0% in online network environments.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 4","pages":"2415-2427"},"PeriodicalIF":6.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492230","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}

{"title":"Mode-Dependent Filtering for Networked Semi-Markov Jump Systems by an AET-Based Round-Robin Protocol","authors":"Wei Qian;Wudi Li;Yanmin Wu;Bin Xu","doi":"10.1109/TNSE.2025.3547935","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3547935","url":null,"abstract":"This article is devoted to the mode-dependent <inline-formula><tex-math>$bm {mathcal {H}}_{infty }$</tex-math></inline-formula> filtering problem for a class of networked semi-Markov jump systems subject to multisensor transmission noises. Considering the constraints of limited bandwidth in practical engineering applications, a new data transmission mechanism of adaptive event triggered-based round-robin protocol is proposed, which can simultaneously save communication resources and reduce data conflicts between the sensor network and the remote filter. Meanwhile, to more accurately describe the complexity of the communication network environment, the data transmission mechanism includes transmission noises, mode information of the systems and semi-Markov switching parameters, which can enhance the flexibility of data transmission. Then, by utilizing the vector augmentation method, a novel mode-dependent <inline-formula><tex-math>$bm {mathcal {H}}_{infty }$</tex-math></inline-formula> filter structure integrating semi-Markov jump modes and sensor scheduling nodes is constructed, which can improve the estimation performance of the filter. Next, by considering the upper bound of sojourn time for all system modes, a non-monotonic Lyapunov function is constructed to get hold of the conservative results by relaxing the monotonic requirement of sojourn time. Based on the semi-definite programming technique and vector augmentation method, sufficient conditions are acquired that guarantee the <inline-formula><tex-math>$bm {sigma }$</tex-math></inline-formula>-error mean-square stability of filtering error dynamics with prescribed <inline-formula><tex-math>$bm {mathcal {H}}_{infty }$</tex-math></inline-formula> performance, and the desired filter parameters can be calculated by solving some recursive linear matrix inequalities. Ultimately, a numerical example and a practical example of F-404 aircraft engine system are carried out to validate the effectiveness and applicability of the proposed filter design strategy.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2373-2387"},"PeriodicalIF":6.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871065","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}

{"title":"ProxaDyn: A Proximity-Aware Dynamic Caching Approach for Named Data Networks","authors":"Matta Krishna Kumari;Nikhil Tripathi;Piyush Joshi","doi":"10.1109/TNSE.2025.3547424","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3547424","url":null,"abstract":"Named Data Network (NDN), a future Internet architecture is introduced to address the shortcomings of the current Internet architecture. NDN supports in-network caching to facilitate scalable content distribution and enhance overall network performance. However, the known NDN caching strategies suffer from a few common drawbacks, such as inefficient cache utilization, high content redundancy, and overhead due to lookup repetition. To address these issues, in this paper, we propose a novel caching strategy called ProxaDyn for efficient content lookup, placement, and replacement. During the content lookup phase, ProxaDyn interacts exclusively with the router responsible for caching a particular content. This eliminates interaction with other intermediate routers, thereby significantly reducing content access latency. For content placement, ProxaDyn strategically selects an on-path router based on content popularity. Popular content is placed in the cache of a router closer to the consumer, while less popular content is cached in a router away from the consumer. This approach significantly improves the cache hits and reduces the access latency. We test ProxaDyn over a diverse range of real-world network topologies. Using extensive experiments, we show that ProxaDyn could achieve significantly better results compared to the state-of-the-art NDN caching strategies.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2360-2372"},"PeriodicalIF":6.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870988","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}

{"title":"Blockchain Assisted Industrial Data Registration and Reconstruction Management Scheme","authors":"Zewei Liu;Chunqiang Hu;Ruifeng Zhao;Pengfei Hu;Arwa Alrawais;Tao Xiang","doi":"10.1109/TNSE.2025.3547409","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3547409","url":null,"abstract":"As a typical Industrial Internet of Things (IIOT) application, three-dimensional point cloud reconstruction brings us benefits and convenience. The reconstructed mathematical models can be employed to facilitate precise quality control, which is important for the usage of the reconstructed products. Conversely, traditional reconstruction methods are characterized by inefficiency, and the errors inherent in each phase of the reconstruction chain often remain opaque and vulnerable to tampering. Hence, we propose a blockchain assisted industrial data registration and reconstruction management scheme (BIRMS). First, the tamper-proof and distributed storage characteristics of blockchain are fully utilized to ensure the authenticity and transparency of output errors throughout the reconstruction process. It is worth noting that smart contracts are designed to facilitate the management and query of on-chain data. Then, a novel swarm intelligence algorithm called EGWODA is designed to handle the issue which is low efficiency in the registration step of reconstruction. Finally, simulation results indicate the feasibility and efficiency of the BIRMS.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2345-2359"},"PeriodicalIF":6.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870841","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}

{"title":"A Hybrid EKF/WUFIR Filter for Indoor Localization Integrating INS and UWB Data","authors":"Long Cheng;Jiahe Song;Wenhao Zhao","doi":"10.1109/TNSE.2025.3546918","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3546918","url":null,"abstract":"Due to the complex and variable indoor environment, ultra-wideband (UWB) signal transmission is often obstructed by walls and obstacles, resulting in non-line-of-sight (NLOS), which reduces localization accuracy. Inertial navigation system (INS) is an autonomous navigation system that does not rely on external information and is not affected by NLOS. Therefore, a hybrid EKF/WUFIR filter indoor localization algorithm that integrates INS and UWB data is proposed. The proposed algorithm is composed of three parts: INS localization, UWB localization and data fusion. In the INS localization part, the motion model is used to determine the state of the target in real time using measurement data obtained from the inertial measurement unit (IMU). In the UWB localization part, a resettable residual weighted particle filter algorithm is proposed to mitigate the effect of NLOS on the localization results. In the data fusion part, a hybrid filtering algorithm combining extended Kalman filter (EKF) and weighted unbiased finite impulse response (WUFIR) filtering is proposed to fuse the INS and UWB localization data. Simulation and experimental results show that the proposed algorithm outperforms other comparative algorithms in terms of robustness and localization accuracy.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2266-2276"},"PeriodicalIF":6.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871070","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}

{"title":"Online Influence Maximization With Semi-Bandit Feedback Under Corruptions","authors":"Xiaotong Cheng;Behzad Nourani-Koliji;Setareh Maghsudi","doi":"10.1109/TNSE.2025.3547240","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3547240","url":null,"abstract":"In this article, we investigate the online influence maximization in social networks. Most prior research studies on online influence maximization assume that the nodes are fully cooperative and act according to their stochastically generated influence probabilities on others. In contrast, we study the online influence maximization problem in the presence of some corrupted nodes whose damaging effects diffuse throughout the network. We propose a novel bandit algorithm, CW-IMLinUCB, which robustly learns and finds the optimal seed set in the presence of corrupted users. Theoretical analyses establish that the regret performance of our proposed algorithm is better than the state-of-the-art online influence maximization algorithms. Extensive empirical evaluations on synthetic and real-world datasets also show the superior performance of our proposed algorithm.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2308-2321"},"PeriodicalIF":6.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870999","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}

{"title":"Achievable Rate Optimization of RIS-Assisted Multi-Antenna FD DF Relay Cooperation System With SWIPT Technology","authors":"Shunwai Zhang;Qingzhu Ma;Hao Cheng;Rongfang Song","doi":"10.1109/TNSE.2025.3546759","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3546759","url":null,"abstract":"To pursue higher achievable rate and wider coverage transmission in wireless communications, this paper proposes a novel reconfigurable intelligent surfaces (RIS)-assisted multi-antenna full-duplex (FD) decode-and-forward (DF) relay cooperation system with simultaneous wireless information and power transfer (SWIPT) technology, which can fully enjoy the advantages of both RIS and SWIPT-based FD DF relay with multiple antennas. In order to maximize the achievable rate of the proposed system, the phase shifts of RIS, the precoding vector and the power splitting factor are jointly optimized. At first, optimal phase shifts of RIS are achieved via aligning the phases of received signals at the destination. Subsequently, the alternating optimization (AO)-based algorithm is adopted to decompose the original optimization problem into two sub-problems, i.e., the precoding vector optimization and the power splitting factor optimization. The sub-problems are still complicated and nonconvex, and the successive convex approximation (SCA) method is applied to reformulate them into convex problems which can be further solved by iterative method. Simulation results illustrate the advantages of the proposed system and reveal the effects of various factors on its performance. Simulation results also demonstrate the superiorities of the joint optimization algorithm compared with its counterparts.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2243-2253"},"PeriodicalIF":6.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143871036","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}

{"title":"Enhancing Graph Structure Learning via Motif-Driven Hypergraph Construction","authors":"Jia-Le Zhao;Xian-Jie Zhang;Xiao Ding;Xingyi Zhang;Hai-Feng Zhang","doi":"10.1109/TNSE.2025.3547349","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3547349","url":null,"abstract":"Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on pairwise graphs often limits their capacity to capture latent higher-order topological semantic information. Thus, it is crucial to find a way to extract the latent higher-order information of graphs without missing the lower-order information of the original graph. To address this issue, we here develop a method to construct hypergraph based on motifs, and then a novel neural network framework, named MD-HGNN, is proposed for enhanced graph learning. Specifically, we first utilize motifs of the original graph to construct the hypergraph and eliminate nested structures within the hypergraph to prevent information redundancy. Subsequently, GNNs and hypergraph neural networks (HGNNs) are employed separately to extract the lower-order and higher-order topological semantic information of the graph. Finally, the lower-order and higher-order information are integrated to obtain an embedded representation of graph. Extensive experimental results demonstrate that MD-HGNN preserves the original lower-order graph structure information while effectively extracting higher-order features. Moreover, its performance and robustness are validated across different downstream tasks.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"12 3","pages":"2333-2344"},"PeriodicalIF":6.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143870990","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}