IEEE Transactions on Network Science and Engineering最新文献

{"title":"Enhanced Quantum Communication With Entanglement Twice Routing and Fidelity Improvement","authors":"Zhongrui Huang;Hong Lai;Linchun Wan","doi":"10.1109/TNSE.2025.3624454","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3624454","url":null,"abstract":"Entanglement networks enable multi-user quantum communication by distributing entangled photon pairs. However, challenges such as uncertain entanglement establishment, resource contention, and fidelity loss hinder their performance. While existing studies address throughput and fidelity issues individually, an integrated approach remains absent. This paper presents the Entanglement Twice Routing with Fidelity Improvement (ETRFI) algorithm, a novel solution to simultaneously enhance throughput and fidelity. ETRFI dynamically allocates supplemental resources based on incremental throughput improvement and purification efficiency. In its second routing phase, entangled links are optimized to balance loss minimization and flow maximization. Furthermore, a time-slot control mechanism ensures synchronization and prevents decoherence during communication. Simulations confirm ETRFI's superior performance, with a 7% increase in entangled pair throughput and a 10% boost in valid throughput, outperforming prior methods.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"3017-3034"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148510749","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":"Hierarchical Forwarding Resource Allocation With Proactive Frame Dropping for VR Video Streaming","authors":"Peng Yang;Qingxuan Feng;Zhixuan Huang;Jiayin Chen;Ning Zhang","doi":"10.1109/TNSE.2025.3593299","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3593299","url":null,"abstract":"In this paper, a hierarchical forwarding resource allocation scheme with proactive frame dropping is proposed for multi-user virtual reality (VR) video streaming, aiming to enhance video quality in the presence of network congestion caused by multiple VR flows sharing a certain hop while considering the impact of network fluctuation. Firstly, we assess the queuing delay bound of frames, allowing bottleneck node to preemptively discard frames that are anticipated to expire. Secondly, we model the priority of different frames based on encoding referencing relationship and users' viewpoints, which helps to prioritize frames of each flow. Subsequently, we formulate an optimization problem to minimize the long-term quality loss caused by frame dropping, while adhering to per-flow quality assurance and total resource constraints. Considering the distinct timescales of network fluctuation and frame dropping, we propose a two-timescale hierarchical resource allocation scheme. On the long timescale, a resource allocation algorithm is designed based on queuing theory to calculate the resource demand of each flow. On the short timescale, to accommodate network fluctuations, a low-complexity heuristic algorithm is devised to adjust allocation results based on frame priority. Comprehensive simulation results validate the effectiveness of the proposed scheme in improving the quality of VR flow under diverse network conditions.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"1243-1257"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148511050","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":"GenAI-Enabled Residual Motion Estimation for Energy-Efficient Semantic Video Communication","authors":"Shavbo Salehi;Pedro Enrique Iturria-Rivera;Medhat Elsayed;Majid Bavand;Yigit Ozcan;Melike Erol-Kantarci","doi":"10.1109/TNSE.2025.3647916","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3647916","url":null,"abstract":"Semantic communication addresses the limitations of the Shannon paradigm by focusing on transmitting meaning rather than exact representations, thereby reducing unnecessary resource consumption. This is particularly beneficial for video, which dominates network traffic and demands high bandwidth and power, making semantic approaches ideal for conserving resources while maintaining quality. In this paper, we propose a Predictability-aware and Entropy-adaptive Neural Motion Estimation (PENME) method to address challenges related to high latency, high bitrate, and power consumption in video transmission. PENME makes per-frame decisions to select a residual motion extraction model, convolutional neural network, vision transformer, or optical flow, using a five-step policy based on motion strength, global motion consistency, peak sharpness, heterogeneity, and residual error. The residual motions are then transmitted to the receiver, where the frames are reconstructed via motion-compensated updates. Next, a selective diffusion-based refinement, the Latent Consistency Model (LCM-4), is applied on frames that trigger refinement due to low predictability or large residuals, while predictable frames skip refinement. PENME also allocates radio resource blocks with awareness of residual motion and channel state, reducing power consumption and bandwidth usage while maintaining high semantic similarity. Our simulation results on the Vimeo90 K dataset demonstrate that the proposed PENME method handles various types of video, outperforming traditional communication, hybrid, and adaptive bitrate semantic communication techniques, achieving 40% lower latency, 90% less transmitted data, and 35% higher throughput. For semantic communication metrics, PENME improves peak signal-to-noise ratio (PSNR) by about 40%, increases multi-scale structural similarity (MS-SSIM) by roughly 19%, and reduces learned perceptual image patch similarity (LPIPS) by nearly 35%, compared with the baseline methods.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5385-5402"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148511101","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":"CSI-Based Respiratory Pattern Recognition Using Diffusion Model and Deep Learning","authors":"Xiaolong Yang;Tingting Zhang;Mu Zhou;Bingcai Chen;Yong Wang;Ruixuan Tong","doi":"10.1109/TNSE.2025.3638894","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3638894","url":null,"abstract":"Respiratory patterns are important indicators of human health, and using AI models to analyze channel state information (CSI) for non-contact respiratory detection shows great potential. However, these methods suffer from limited data, which reduces both the detection accuracy and the model's generalization. Generative AI offers a novel solution to this issue, which can efficiently generate high-quality respiratory data, alleviate the problem of data scarcity and improve model performance. This study proposes an innovative approach that combines a diffusion model with a convolutional-transformer (CNN-Transformer) network model to achieve reliable recognition of respiratory patterns under data-scarce conditions. We first design a set of data preprocessing methods, which converts CSI amplitude and phase into 1D time series representing respiratory patterns. Then, we apply the diffusion model for data augmentation to generate diverse and high-quality RF data. Subsequently, the preprocessed data are mapped into 2D images using the gramian angular field (GAF) method and then fed into a CNN-Transformer network model to classify three types of respiratory patterns. Experimental results demonstrate that the proposed approach can effectively enhance the performance of respiratory pattern recognition. When combined with the CNN-Transformer model, the accuracy, precision, recall, and specificity are 98.3%, 98.3%, 98.0%, and 98.8%, respectively, which are improved by 3%-4% compared with the CNN, ResNet-18, and long short-term memory (LSTM) baseline models. This approach validates the feasibility of respiratory pattern recognition and provides robust support for the early diagnosis and monitoring of related diseases.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"3894-3910"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148511243","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":"Joint Data Compression, Secure Multi-Part Collaborative Task Offloading and Resource Assignment in Ultra-Dense Networks","authors":"Tianqing Zhou;Kangle Liu;Xuan Li;Nan Jiang;Chunguo Li","doi":"10.1109/TNSE.2025.3582977","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3582977","url":null,"abstract":"To enhance resource utilization and address interference issues in ultra-dense networks with mobile edge computing (MEC), a resource utilization approach is first introduced, which integrates orthogonal frequency division multiple access (OFDMA) and non-orthogonal multiple access (NOMA). Then, to minimize the energy consumed by ultra-densely deployed small base stations (SBSs) while ensuring proportional assignment of computational resources and the constraints related to processing delay and security breach cost, the joint optimization of channel selection, the number of subchannels, secure service assignment, multi-step computation offloading, device association, data compression (DC) control, power control, and frequency band partitioning is done for minimizing network-wide energy consumption (EC). Given that the current problem is nonlinear and involves integral optimization parameters, we have devised an adaptive genetic water wave optimization (AGWWO) algorithm by improving the traditional water wave optimization (WWO) algorithm using genetic operations. After that, the computational complexity, convergence, and practical implementation of AGWWO algorithm are analyzed. Simulation results reveal that this algorithm effectively reduces network-wide EC while guaranteeing the constraints of processing delay and security breach cost.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"278-295"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512083","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":"Secure State Estimation: A Novel Retransmission and Roll-Back Mechanism","authors":"Hongyuan Xu;Wei Wang;Jiaqi Yan;Qing Gao","doi":"10.1109/TNSE.2025.3621283","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3621283","url":null,"abstract":"As the scale of networked control systems gradually expands, an increasing number of sensors are installed to monitor the operational status of the systems. This paper focuses on the problem of secure state estimation for multi-sensor networked control systems under cyber attacks, utilizing available retransmitted information. Firstly, we establish retransmission rules for data considered suspicious due to additive external attacks, based on which a novel state estimation method called distributed retransmission and roll-back estimator (DRR-Estimator) is proposed. More specifically, this designed estimator incorporates a term for historical discarded data, which is able to enhance the estimation performance. Using linear matrix inequalities, we prove that the estimation error converges to a finite bound exponentially in mean square, even in the presence of attacks. It is further demonstrated that the proposed estimator works effectively under time delays. Finally, the proposed method is tested effectively in a regional temperature monitoring system.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"2852-2870"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512478","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":"DVA-ACLF: Designated Verifier Asymmetric Anonymous Credential Scheme for Low-Altitude Flight","authors":"Biwen Chen;Qiuling Guan;Jia Li;Jing Wang;Jialing He;Debiao He;Tao Xiang","doi":"10.1109/TNSE.2025.3649720","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3649720","url":null,"abstract":"Low-altitude aerial networks play an increasingly critical role in urban logistics, emergency response, and intelligent airspace management. These networks must ensure reliable identity authentication while addressing privacy, efficiency, and security challenges. Traditional authentication mechanisms—such as PKI-based certificates and remote identification—either overexpose identity data or lack support for flexible attribute disclosure. Moreover, existing anonymous credential (AC) schemes, though offering privacy, often incur high computational and communication costs and exist vulnerable to replay attacks. To address these limitations, we propose <bold>DVA-ACLF</b> (<italic>Designated Verifier Asymmetric Anonymous Credential Scheme for Low-Altitude Flight</i>), a lightweight credential system tailored for AAVs. DVA-ACLF leverages an efficient MAC-based construction combined with non-interactive zero-knowledge proofs to enable selective attribute disclosure without relying on pairings. It supports designated verification to prevent credential misuse and replay. Experimental results show that DVA-ACLF reduces credential size by 49% (2720 bits vs. 5369 bits in Idemix) and mitigates the linear growth of credential size with respect to attribute count observed in CL and BBS+ schemes. It requires only four single-base and <inline-formula><tex-math>$2(k-l)$</tex-math></inline-formula> double-base exponentiations on AAVs, where <inline-formula><tex-math>$k$</tex-math></inline-formula> is the number of selected attributes and <inline-formula><tex-math>$l$</tex-math></inline-formula> is the number of disclosed attributes—both typically small. These features make DVA-ACLF a strong candidate for secure, efficient, and privacy-preserving authentication in low-altitude flight applications.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"5752-5767"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148512445","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":"AAV Video Encoding: Online Rate-Distortion Modeling and Multi-Timescale Rate Control","authors":"Zongyang Yu;Xianbin Cao;Peng Yang;Dezhi Zheng;Haijun Zhang;Tony Q. S. Quek;Dapeng Oliver Wu","doi":"10.1109/TNSE.2025.3600297","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3600297","url":null,"abstract":"This paper is concerned with rate control (RC) for autonomous aerial vehicle (AAV) video encodingto produce a stable video bitrate and high quality videos under specific constraints. To this aim, we theoretically investigate the relationships between encoding parameters and bitrate and distortion, and design an accurate online rate-distortion (R-D) model by employing linear regression and predictive artificial intelligence (AI) methods. Considering the sensitivity of AAV video encoding to latency and power consumption, we further establish the mathematical relationships between encoding parameters and encoding time and power consumption. By integrating the R-D and encoding time and power consumption models, we formulate a multi-timescale optimization problem and propose a novel algorithm to solve it. Specifically, we decompose it into a family of single-timescale problems via an alternating direction method of multipliers (ADMM). In addition, we design an iterative optimization scheme to solve the single-timescale problems with low computational complexity. Extensive experiments are conducted to validate the designed model and algorithm. Experimental results indicate that the designed model has small estimation errors, and the variance of Y-PSNR achieved by the proposed algorithm is not greater than 26.3% of its counterpart.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"1723-1737"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513292","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":"Graph Learning-Based Multi-Dimensional Adaptive Scheduling for Multi-QoS Remote Sensing Services in Satellite Networks","authors":"Zhe Niu;Hui Yang;Qiuyan Yao;Yuxuan Yan;Buzheng Wei;Jie Zhang","doi":"10.1109/TNSE.2026.3689124","DOIUrl":"https://doi.org/10.1109/TNSE.2026.3689124","url":null,"abstract":"With the rapid development of inter-satellite laser links, Very low Earth orbit (VLEO) constellations are showing a clear trend of intra-layer networking. As a result, RS transmission is no longer limited to the low Earth orbit (LEO) segment, and the optimization scope should be extended to the full VLEO–LEO process. However, full-process scheduling for multi-QoS RS services faces two major challenges. First, it is highly sensitive to environmental dynamics, since changes in network state may directly cause a group of services to shift from QoS-satisfied to QoS-violated. Second, multiple processes, including inter-layer topology formation, link latency mapping, and scheduling parameter adaptation, form a coupled decision chain that jointly determines the final scheduling result. This imposes strong demands on a system-level adaptive scheduling framework. To address these challenges, this paper proposes a Multi-dimensional Graph representation learning-driven Scheduling and Routing (MGSR) framework. MGSR adopts a hierarchical and collaborative Graph Representation Learning (GRL) architecture to enable system-level adaptive scheduling across multiple dimensions. It includes adaptive uplink node selection, spatiotemporal link latency mapping, and QoS-aware iterative scheduling into a unified decision process. By leveraging graph neural networks to capture topology evolution and traffic dynamics, MGSR can rapidly adjust routing and scheduling strategies under varying service ratios, traffic intensities, and constellation topologies. The simulations show that MGSR consistently outperforms state-of-the-art routing algorithms, achieving significant improvements in QoS satisfaction ratio.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"9413-9429"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148513618","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":"Integrated Sensing and Backscatter Communication With Movable Antennas: State-of-the-Art Survey and a Novel Inverse Scattering Framework","authors":"Yi Fang;Junxiang Yang;Dingfei Ma;Min Yang;Zihao Xu;Xinhao Chen","doi":"10.1109/TNSE.2025.3598575","DOIUrl":"https://doi.org/10.1109/TNSE.2025.3598575","url":null,"abstract":"In this paper, we put forward a state-of-the-art survey and a novel inverse scattering framework for integrated sensing and backscatter communication with movable antennas. Specifically, the proposed system employs inverse scattering-based sensing techniques for accurate tag localization and identification, combined with spatial modulation (SM) and quadrature spatial modulation (QSM) methods to facilitate reliable backscatter communication. We first establish an electromagnetic system model incorporating structural and antenna modes of the tags as well as clutter scatterers in the environment. By dynamically adjusting the load impedances of the tags, these structural and antenna mode components can be effectively separated. The structural and antenna mode components processed by the compressed sensing (CS) algorithms and the inverse scattering methods can respectively achieve high-precision localization of clutter scatterers and tags, as well as accurate identification of tags. In addition, channel state information (CSI) derived from antenna mode analysis during the localization and identification processes is utilized to seamlessly integrate SM and QSM techniques into the backscatter communication framework. The proposed integrated scheme achieves more robust and efficient transmission performance compared to conventional vertical bell laboratories layered space-time (V-BLAST) and single-input multiple-output (SIMO) schemes. To validate the feasibility and effectiveness of the proposed approach, we implement a proof-of-principle illustrative system comprising one transmit and one receive array, each configured as a <inline-formula><tex-math>$3times 3$</tex-math></inline-formula> uniform linear array (ULA). By evaluating the sensing capacity metric, we demonstrate that system performance can be further improved through the integration of movable antenna (MA) techniques. Simulation results confirm that the proposed system achieves high-quality localization and identification of both single and multiple tags in cluttered environments, while simultaneously ensuring reliable backscatter communication.","PeriodicalId":54229,"journal":{"name":"IEEE Transactions on Network Science and Engineering","volume":"13 ","pages":"4350-4368"},"PeriodicalIF":7.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148514051","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}